build the site docs
building documentation with nbconvert and dataframes.
import tonyfast.utils , pandas , json , nbconvert , nbformat , operator , bs4 , anyio , pathlib , re
__import__ ( "nest_asyncio" ) . apply ()
configure the accessible exporter for our notebooks
exporter = nbconvert . get_exporter ( "a11y" )(
exclude_input_prompt = True ,
include_sa11y = False ,
exclude_output_prompt = True ,
hide_anchor_links = True ,
include_settings = True
)
_ , resources = exporter . from_notebook_node ( nbformat . v4 . new_notebook ( cells = []))
ours , theirs = pandas . Series () . template . environment , exporter . environment
ours . loader = theirs . loader
ours . filters . update ({ ** theirs . filters , ** ours . filters })
ours . globals . update ({ ** theirs . globals , ** ours . globals })
async def from_notebook_node ( nb , resources = None , exporter = exporter ):
return exporter . from_notebook_node ( nb , resources = resources )[ 0 ]
load in all the notebooks
%%
load in all the notebooks
find all the notebooks resembling a post .
we are skipping the work needing to be done on the indexes and readmes .
indexes and readmes use different exporter configurations than content notebooks .
df = await pandas . Index ([ "tonyfast" ]) . apath () . apath . rglob ( "[0-9][0-9][0-9][0-9]-*.ipynb" )
remove checkpoint files .
df = df [ ~ df . astype ( str ) . str . contains ( "checkpoint" )] . pipe ( pandas . Index ) . rename ( "file" )
extract the date from the title . this can later be enriched with git information
TITLE = "(?P<year>[0-9] {4} )-(?P<month>[0-9] {2} )-(?P<day>[0-9]{1,2})-(?P<slug>.+)"
df = df . apath . stem . str . extract ( TITLE ) . set_index ( df ) . dropna ( how = "all" )
df [ "date" ] = pandas . to_datetime ( df . year + "-" + df . month + "-" + df . day )
df = df . sort_values ( "date" , ascending = False )
read in all the notebooks
df = ( await df . index . apath . read_text ()) . apply ( json . loads ) . rename ( "nb" ) . apply ( nbformat . from_dict ) . pipe ( df . join ) . head ( 10 )
df
load in all the notebooks
find all the notebooks resembling a post.
we are skipping the work needing to be done on the indexes and readmes.
indexes and readmes use different exporter configurations than content notebooks.
df = await pandas.Index(["tonyfast"]).apath().apath.rglob("[0-9][0-9][0-9][0-9]-*.ipynb")
remove checkpoint files.
df = df[~df.astype(str).str.contains("checkpoint")].pipe(pandas.Index).rename("file")
extract the date from the title. this can later be enriched with git information
TITLE = "(?P<year>[0-9]{4})-(?P<month>[0-9]{2})-(?P<day>[0-9]{1,2})-(?P<slug>.+)"
df = df.apath.stem.str.extract(TITLE).set_index(df).dropna(how="all")
df["date"] = pandas.to_datetime(df.year +"-"+ df.month +"-"+ df.day)
df = df.sort_values("date", ascending=False)
read in all the notebooks
df = (await df.index.apath.read_text()).apply(json.loads).rename("nb").apply(nbformat.from_dict).pipe(df.join).head(10)
df
year
month
day
slug
date
nb
file
tonyfast/xxiv/2024-02-21-build-docs-pd.ipynb
2024
02
21
build-docs-pd
2024-02-21
{'cells': [{'cell_type': 'markdown', 'id': '55...
tonyfast/xxiv/2024-02-20-multiindex-html.ipynb
2024
02
20
multiindex-html
2024-02-20
{'cells': [{'cell_type': 'markdown', 'id': 'd0...
tonyfast/xxiv/2024-02-15-pandas-table-names.ipynb
2024
02
15
pandas-table-names
2024-02-15
{'cells': [{'cell_type': 'markdown', 'id': '98...
tonyfast/xxiv/2024-02-05-aiohttp-pandas-accessor.ipynb
2024
02
05
aiohttp-pandas-accessor
2024-02-05
{'cells': [{'cell_type': 'markdown', 'id': '44...
tonyfast/xxiv/2024-01-25-mast-a11y-notebooks.ipynb
2024
01
25
mast-a11y-notebooks
2024-01-25
{'cells': [{'attachments': {}, 'cell_type': 'm...
tonyfast/xxiv/2024-01-20-colgroup-schema.ipynb
2024
01
20
colgroup-schema
2024-01-20
{'cells': [{'cell_type': 'markdown', 'id': '8c...
tonyfast/xxiv/2024-01-05-docx-to-data.ipynb
2024
01
05
docx-to-data
2024-01-05
{'cells': [{'cell_type': 'markdown', 'id': '2a...
tonyfast/xxiii/2024-01-05-docx-to-data.ipynb
2024
01
05
docx-to-data
2024-01-05
{'cells': [{'cell_type': 'markdown', 'id': '2a...
tonyfast/xxiii/2023-12-20-interactive-html-validation.ipynb
2023
12
20
interactive-html-validation
2023-12-20
{'cells': [{'cell_type': 'markdown', 'id': 'a2...
tonyfast/xxiii/2023-12-18-ugghhh.ipynb
2023
12
18
ugghhh
2023-12-18
{'cells': [{'cell_type': 'markdown', 'id': '1a...
the notebooks require preparation before they can transform to html
is_midgy = re . compile ( "^\%\%[\s+,(pidgy),(midgy)]" )
def prepare_cell ( cell ):
"""make inplace changes to the notebook that carried through the publishing process"""
cell . source = "" . join ( cell . source )
if is_midgy . match ( cell . source ):
cell . metadata . setdefault ( "jupyter" , {})[ "source_hidden" ] = True
for out in cell . get ( "outputs" , "" ):
for k , v in out . get ( "data" , {}) . items (): k . startswith ( "text" ) and out [ "data" ] . __setitem__ ( k , "" . join ( v ))
if "text" in out : out . text = "" . join ( out . text )
return cell
cells = df . nb . itemgetter ( "cells" ) . enumerate ( "index" ) . apply ( prepare_cell ) . series ()
def render_markdown_output ( output ):
if "data" in output :
if "text/markdown" in output [ "data" ]:
md = exporter . environment . globals [ "markdown" ]( output [ "data" ][ "text/markdown" ])
output [ "data" ][ "text/html" ] = md
return md
outputs = cells . outputs . dropna () . enumerate ( "output" ) . dropna ()
outputs . apply ( render_markdown_output );
markdowns = cells [ cells . cell_type . eq ( "markdown" )] . apply (
lambda s : operator . setitem (
s . metadata . setdefault ( "data" , {}),
"text/html" ,
html := exporter . environment . globals [ "markdown" ]( s . source ),
)
or html ,
axis = 1 ,
) . to_frame ( "html" ) . assign ( output =- 1 ) . set_index ( "output" , append = True )
%%
create intermediate representations of markdown . when we handle this work before templating we can use partial information from the outcome
to build the table of contents and relative links .
html = pandas . concat (
[
markdowns ,
outputs . itemgetter ( "data" ) . dropna () . itemgetter ( "text/html" ) . dropna () . to_frame ( "html" ),
]
) . sort_index ()
html [ "soup" ] = html . html . apply ( bs4 . BeautifulSoup , features = "lxml" )
extract the headings from each cell
html [ "h" ] = html . soup . methodcaller ( "select" , "h1,h2,h3,h4,h5,h6" )
h = html . h . enumerate ( "h" ) . dropna ()
expand the headings into features on the dataframe
h = h . to_frame ( "h" ) . assign (
level = h . attrgetter ( "name" ) . str . lstrip ( "h" ) . astype ( int ),
string = h . attrgetter ( "string" ),
id = h . attrgetter ( "attrs" ) . itemgetter ( "id" )
); h . head ()
create intermediate representations of markdown. when we handle this work before templating we can use partial information from the outcome
to build the table of contents and relative links.
html = pandas.concat(
[
markdowns,
outputs.itemgetter("data").dropna().itemgetter("text/html").dropna().to_frame("html"),
]
).sort_index()
html["soup"] = html.html.apply(bs4.BeautifulSoup, features="lxml")
extract the headings from each cell
html["h"] = html.soup.methodcaller("select", "h1,h2,h3,h4,h5,h6")
h = html.h.enumerate("h").dropna()
expand the headings into features on the dataframe
h = h.to_frame("h").assign(
level=h.attrgetter("name").str.lstrip("h").astype(int),
string=h.attrgetter("string"),
id=h.attrgetter("attrs").itemgetter("id")
); h.head()
h
level
string
id
file
index
output
h
tonyfast/xxiii/2023-12-18-ugghhh.ipynb
0
-1
0
[slicing and dicing accessibility violations w...
1
slicing and dicing accessibility violations wi...
slicing-and-dicing-accessibility-violations-wi...
8
-1
0
[unexpected failures]
2
unexpected failures
unexpected-failures
11
-1
0
[expected failures]
2
expected failures
expected-failures
13
-1
0
[unexpected passes]
2
unexpected passes
unexpected-passes
14
-1
0
[unexpected passes and failures]
2
unexpected passes and failures
unexpected-passes-and-failures
%%
extract the document title from the headings . _we should probably extract a description too .
adding description to the meta is good for accessibility when choosing tabs .
df = df . assign ( title = h . groupby ( h . index . get_level_values ( "file" )) . apply (
lambda s : s . sort_values ( "level" ) . string . iloc [ 0 ]
) . rename ( "title" ))
extract the document title from the headings. _we should probably extract a description too.
adding description to the meta is good for accessibility when choosing tabs.
df = df.assign(title=h.groupby(h.index.get_level_values("file")).apply(
lambda s: s.sort_values("level").string.iloc[0]
).rename("title"))
%%
def make_toc ( df ):
make a table of contents ` details & gt ; nav & gt ; ol ` for a dataframe
toc = bs4 . BeautifulSoup ( features = "lxml" )
toc . append ( nav := toc . new_tag ( "nav" ))
nav . append ( ol := toc . new_tag ( "ol" ))
last_level = 1
for i , row in df . iterrows ():
if row . level & gt ; last_level :
for i in range ( last_level , row . level ):
last_level = i + 1
ol . append ( li := toc . new_tag ( "li" ))
li . append ( ol := toc . new_tag ( "ol" ))
else :
for i in range ( row . level , last_level ):
ol = ol . parent . parent
ol . append ( li := toc . new_tag ( "li" ))
li . append ( a := toc . new_tag ( "a" ))
a . append ( row . string )
a . attrs . update ( href = F "# { row . id } " )
return toc
generate the table of contents for each file we have indexed
df = df . assign ( toc = h . groupby ( h . index . get_level_values ( "file" )) . apply ( make_toc ) . apply ( str ))
%%
determine the location of the html version of the file .
target = anyio . Path ( "outputs" )
df = df . assign ( target = df . index . map ( target . __truediv__ ) . apath . with_suffix ( ".html" ) . values )
determine the location of the html version of the file.
target = anyio.Path("outputs")
df = df.assign(target=df.index.map(target.__truediv__).apath.with_suffix(".html").values)
df = df . assign ( ** pandas . DataFrame ([
[ None ] + df . index . values [: - 1 ] . tolist (), df . index . values , df . index . values [ 1 :] . tolist () + [ None ]
], index = [ "prev" , "file" , "next" ]) . T . set_index ( "file" ))
def relative_path ( source , target ):
"""compute a relative path from source to target"""
if target :
common = []
source = pathlib . Path ( source ) . absolute ()
target = pathlib . Path ( target ) . absolute ()
for i , parent in enumerate ( source . parents ):
if parent == target . parents [ i ]:
common . append ( parent )
return type ( source )( * [ ".." ] * ( len ( source . parents ) - len ( common )), * target . parts [ len ( common ):])
%%
generate the footer that contains the previous and next links
df = df . assign (
footer = df . apply (
lambda s : ( s . prev and F """<a href=" { relative_path ( s . target , df . loc [ s . prev ] . target ) } " rel="prev><span aria-hidden=" true"="">< { df . loc [ s . prev ] . title } </a><br/>""" or "" )
+ ( s . next and F """<a href=" { relative_path ( s . target , df . loc [ s . next ] . target ) } " rel="next"> { df . loc [ s . next ] . title } <span aria-hidden="true">></span></a><br/>""" or "" ),
axis = 1
)
)
generate the footer that contains the previous and next links
df = df.assign(
footer = df.apply(
lambda s: (s.prev and F"""<a href="{relative_path(s.target, df.loc[s.prev].target)}" rel="prev><span aria-hidden=" true"=""><{df.loc[s.prev].title}</a><br/>""" or "")
+ (s.next and F"""<a href="{relative_path(s.target, df.loc[s.next].target)}" rel="next">{df.loc[s.next].title} <span aria-hidden="true">></span></a><br/>""" or ""),
axis=1
)
)
df = df . assign (
header = df . apply (
lambda s : "<details><summary>site navigation</summary><nav><ol> %s </ol></nav></details>" % "" . join (
F """<li><a href=" { relative_path ( s . target , t . target ) } "> { t . title } </a></li>"""
for i , t in df . iterrows ()
), axis = 1
))
df [ "html" ] = await df [[ "nb" ]] . apply (
lambda s : from_notebook_node ( s [ "nb" ], dict ( toc = df . toc . loc [ s . name ], footer = df . loc [ s . name ] . footer , header = df . loc [ s . name ] . header )), axis = 1 ) . gather ()
df . html . head () . display . iframe () . display ()
create a kernel for the environment to run the notebooks in
```bash
mamba run -p.mast_nb python -m ipykernel install --user --name mast_nb
```</textarea>
<div aria-labelledby="nb-source-label" aria-roledescription="highlighted" role="group">
<div class="highlight">
<pre><span></span><code><span class="o">%%</span>
<span class="n">create</span> <span class="n">an</span> <span class="n">environment</span><span class="o">.</span><span class="n">yml</span> <span class="n">file</span> <span class="kn">from</span> <span class="nn">the</span> <span class="n">verions</span> <span class="n">information</span> <span class="n">previously</span> <span class="n">collected</span>
<span class="kn">import</span> <span class="nn">yaml</span><span class="p">;</span> <span class="kn">from</span> <span class="nn">pathlib</span> <span class="kn">import</span> <span class="n">Path</span>
<span class="n">deps</span> <span class="o">=</span> <span class="n">versions</span><span class="o">.</span><span class="n">package</span><span class="o">.</span><span class="n">dropna</span><span class="p">()</span><span class="o">.</span><span class="n">drop_duplicates</span><span class="p">()</span><span class="o">.</span><span class="n">tolist</span><span class="p">()</span>
<span class="n">deps</span> <span class="o">=</span> <span class="p">[{</span><span class="s2">"git"</span><span class="p">:</span> <span class="s2">"GitPython"</span><span class="p">}</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">deps</span> <span class="p">]</span>
<span class="n">Path</span><span class="p">(</span><span class="s2">"environment.yml"</span><span class="p">)</span><span class="o">.</span><span class="n">write_text</span><span class="p">(</span><span class="n">yaml</span><span class="o">.</span><span class="n">safe_dump</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span>
<span class="n">name</span><span class="o">=</span><span class="s2">"mast_notebooks"</span><span class="p">,</span>
<span class="n">channels</span><span class="o">=</span><span class="p">[</span><span class="s2">"conda-forge"</span><span class="p">],</span>
<span class="n">dependencies</span><span class="o">=</span><span class="p">[</span><span class="s2">"pip"</span><span class="p">,</span> <span class="nb">dict</span><span class="p">(</span>
<span class="n">pip</span><span class="o">=</span><span class="n">deps</span><span class="o">+</span> <span class="p">[</span><span class="s2">"ipykernel"</span><span class="p">,</span> <span class="s2">"astrocut"</span><span class="p">]</span>
<span class="p">)]</span>
<span class="p">)))</span>
<span class="n">uncomment</span> <span class="n">the</span> <span class="n">code</span> <span class="n">below</span> <span class="n">to</span> <span class="n">create</span> <span class="ow">or</span> <span class="n">update</span> <span class="n">the</span> <span class="n">environment</span> <span class="n">the</span> <span class="n">environment</span>
<span class="err">```</span><span class="n">bash</span>
<span class="n">mamba</span> <span class="n">env</span> <span class="n">create</span> <span class="o">-</span><span class="n">p</span><span class="o">.</span><span class="n">mast_nb</span> <span class="o">-</span><span class="n">f</span> <span class="n">environment</span><span class="o">.</span><span class="n">yml</span>
<span class="n">mamba</span> <span class="n">env</span> <span class="n">update</span> <span class="o">-</span><span class="n">p</span><span class="o">.</span><span class="n">mast_nb</span> <span class="o">-</span><span class="n">f</span> <span class="n">environment</span><span class="o">.</span><span class="n">yml</span>
<span class="err">```</span>
<span class="n">create</span> <span class="n">a</span> <span class="n">kernel</span> <span class="k">for</span> <span class="n">the</span> <span class="n">environment</span> <span class="n">to</span> <span class="n">run</span> <span class="n">the</span> <span class="n">notebooks</span> <span class="ow">in</span>
<span class="err">```</span><span class="n">bash</span>
<span class="n">mamba</span> <span class="n">run</span> <span class="o">-</span><span class="n">p</span><span class="o">.</span><span class="n">mast_nb</span> <span class="n">python</span> <span class="o">-</span><span class="n">m</span> <span class="n">ipykernel</span> <span class="n">install</span> <span class="o">--</span><span class="n">user</span> <span class="o">--</span><span class="n">name</span> <span class="n">mast_nb</span>
<span class="err">```</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-14-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-14" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-14-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{'jupyter': {'source_hidden': True}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-14-loc" role="none">
25
</td>
<td class="nb-outputs" role="none">
<span class="nb-outputs visually-hidden" id="cell-14-outputs-len">
1 outputs.
</span>
<fieldset class="nb-outputs" data-outputs="1">
<legend aria-describedby="nb-outputs-desc" hidden id="cell-14-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
11
</span>
<span aria-hidden="true">
]
</span>
</legend>
<p>
create an environment.yml file from the verions information previously collected
</p>
<pre><code>import yaml; from pathlib import Path
deps = versions.package.dropna().drop_duplicates().tolist()
deps = [{"git": "GitPython"}.get(x,x) for x in deps ]
Path("environment.yml").write_text(yaml.safe_dump(dict(
name="mast_notebooks",
channels=["conda-forge"],
dependencies=["pip", dict(
pip=deps+ ["ipykernel", "astrocut"]
)]
)))
</code></pre>
<p>
uncomment the code below to create or update the environment the environment
</p>
<pre><code class="bash"><div class="highlight"><pre><span></span><code>mamba<span class="w"> </span>env<span class="w"> </span>create<span class="w"> </span>-p.mast_nb<span class="w"> </span>-f<span class="w"> </span>environment.yml
mamba<span class="w"> </span>env<span class="w"> </span>update<span class="w"> </span>-p.mast_nb<span class="w"> </span>-f<span class="w"> </span>environment.yml
</code></pre></div>
</code></pre>
<p>
create a kernel for the environment to run the notebooks in
</p>
<pre><code class="bash"><div class="highlight"><pre><span></span><code>mamba<span class="w"> </span>run<span class="w"> </span>-p.mast_nb<span class="w"> </span>python<span class="w"> </span>-m<span class="w"> </span>ipykernel<span class="w"> </span>install<span class="w"> </span>--user<span class="w"> </span>--name<span class="w"> </span>mast_nb
</code></pre></div>
</code></pre>
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 15 cell-15-cell_type" class="cell code" data-index="15" data-loc="14" role="listitem">
<th class="nb-anchor" role="none" scope="row">
<a aria-describedby="nb-code-label nb-cell-label cell-15-loc nb-loc-label
cell-15-outputs-len" aria-labelledby="nb-cell-label 15" class="nb-anchor visually-hidden" href="#15" id="15">
15
</a>
</th>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-15-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
12
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label id="cell-15-cell_type">
code
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-15-source-label" class="nb-toolbar" hidden id="cell-15" name="cell-15">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-15-start" role="none">
</td>
<td class="nb-end" hidden id="cell-15-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-15-source-label nb-source-label" class="nb-source" form="cell-15" id="cell-15-source" name="source" readonly rows="14">%%
### executing the notebooks
execute the notebooks using the `nbclient` library
client = notebooks.nb.apply(nbformat.from_dict).apply(
nbclient.NotebookClient, kernel_name="mast_nb", allow_errors=True
)
recombine the executed notebooks with the original ones.
notebooks.nb = (
await client.head(3).apply(nbclient.NotebookClient.async_execute).gather()
).combine_first(notebooks.nb)</textarea>
<div aria-labelledby="nb-source-label" aria-roledescription="highlighted" role="group">
<div class="highlight">
<pre><span></span><code><span class="o">%%</span>
<span class="c1">### executing the notebooks</span>
<span class="n">execute</span> <span class="n">the</span> <span class="n">notebooks</span> <span class="n">using</span> <span class="n">the</span> <span class="err">`</span><span class="n">nbclient</span><span class="err">`</span> <span class="n">library</span>
<span class="n">client</span> <span class="o">=</span> <span class="n">notebooks</span><span class="o">.</span><span class="n">nb</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">nbformat</span><span class="o">.</span><span class="n">from_dict</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span>
<span class="n">nbclient</span><span class="o">.</span><span class="n">NotebookClient</span><span class="p">,</span> <span class="n">kernel_name</span><span class="o">=</span><span class="s2">"mast_nb"</span><span class="p">,</span> <span class="n">allow_errors</span><span class="o">=</span><span class="kc">True</span>
<span class="p">)</span>
<span class="n">recombine</span> <span class="n">the</span> <span class="n">executed</span> <span class="n">notebooks</span> <span class="k">with</span> <span class="n">the</span> <span class="n">original</span> <span class="n">ones</span><span class="o">.</span>
<span class="n">notebooks</span><span class="o">.</span><span class="n">nb</span> <span class="o">=</span> <span class="p">(</span>
<span class="k">await</span> <span class="n">client</span><span class="o">.</span><span class="n">head</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">nbclient</span><span class="o">.</span><span class="n">NotebookClient</span><span class="o">.</span><span class="n">async_execute</span><span class="p">)</span><span class="o">.</span><span class="n">gather</span><span class="p">()</span>
<span class="p">)</span><span class="o">.</span><span class="n">combine_first</span><span class="p">(</span><span class="n">notebooks</span><span class="o">.</span><span class="n">nb</span><span class="p">)</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-15-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-15" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-15-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{'jupyter': {'source_hidden': True}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-15-loc" role="none">
14
</td>
<td class="nb-outputs" role="none">
<span class="nb-outputs visually-hidden" id="cell-15-outputs-len">
2 outputs.
</span>
<fieldset class="nb-outputs" data-outputs="2">
<legend aria-describedby="nb-outputs-desc" hidden id="cell-15-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
12
</span>
<span aria-hidden="true">
]
</span>
</legend>
<h3>
<a href="#executing-the-notebooks">
executing the notebooks
</a>
</h3>
<p>
execute the notebooks using the
<code>
nbclient
</code>
library
</p>
<pre><code>client = notebooks.nb.apply(nbformat.from_dict).apply(
nbclient.NotebookClient, kernel_name="mast_nb", allow_errors=True
)
</code></pre>
<p>
recombine the executed notebooks with the original ones.
</p>
<pre><code>notebooks.nb = (
await client.head(3).apply(nbclient.NotebookClient.async_execute).gather()
).combine_first(notebooks.nb)
</code></pre>
<pre class="stdout">
<code>0.00s - Debugger warning: It seems that frozen modules are being used, which may
0.00s - make the debugger miss breakpoints. Please pass -Xfrozen_modules=off
0.00s - to python to disable frozen modules.
0.00s - Note: Debugging will proceed. Set PYDEVD_DISABLE_FILE_VALIDATION=1 to disable this validation.
0.00s - Debugger warning: It seems that frozen modules are being used, which may
0.00s - make the debugger miss breakpoints. Please pass -Xfrozen_modules=off
0.00s - to python to disable frozen modules.
0.00s - Note: Debugging will proceed. Set PYDEVD_DISABLE_FILE_VALIDATION=1 to disable this validation.
0.00s - Debugger warning: It seems that frozen modules are being used, which may
0.00s - make the debugger miss breakpoints. Please pass -Xfrozen_modules=off
0.00s - to python to disable frozen modules.
0.00s - Note: Debugging will proceed. Set PYDEVD_DISABLE_FILE_VALIDATION=1 to disable this validation.
</code>
</pre>
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 16 cell-16-cell_type" class="cell code" data-index="16" data-loc="12" role="listitem">
<th class="nb-anchor" role="none" scope="row">
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cell-16-outputs-len" aria-labelledby="nb-cell-label 16" class="nb-anchor visually-hidden" href="#16" id="16">
16
</a>
</th>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-16-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
13
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label id="cell-16-cell_type">
code
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-16-source-label" class="nb-toolbar" hidden id="cell-16" name="cell-16">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-16-start" role="none">
</td>
<td class="nb-end" hidden id="cell-16-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-16-source-label nb-source-label" class="nb-source" form="cell-16" id="cell-16-source" name="source" readonly rows="12">%%
merge our default async jinja exporter with the legacy nbconvert importer. the async template is a massive improvement of the sync nbconvert.
exporter = nbconvert.get_exporter("a11y")()
generate the default `resources` object for templating the notebook
_, resources = exporter.from_notebook_node(nbformat.reads("""{"cells": [], "metadata": {}}""", 4))
ours,theirs = Series().template.environment,exporter.environment
ours.loader = theirs.loader
ours.filters.update({**theirs.filters, **ours.filters})
ours.globals.update({**theirs.globals, **ours.globals})
</textarea>
<div aria-labelledby="nb-source-label" aria-roledescription="highlighted" role="group">
<div class="highlight">
<pre><span></span><code><span class="o">%%</span>
<span class="n">merge</span> <span class="n">our</span> <span class="n">default</span> <span class="k">async</span> <span class="n">jinja</span> <span class="n">exporter</span> <span class="k">with</span> <span class="n">the</span> <span class="n">legacy</span> <span class="n">nbconvert</span> <span class="n">importer</span><span class="o">.</span> <span class="n">the</span> <span class="k">async</span> <span class="n">template</span> <span class="ow">is</span> <span class="n">a</span> <span class="n">massive</span> <span class="n">improvement</span> <span class="n">of</span> <span class="n">the</span> <span class="n">sync</span> <span class="n">nbconvert</span><span class="o">.</span>
<span class="n">exporter</span> <span class="o">=</span> <span class="n">nbconvert</span><span class="o">.</span><span class="n">get_exporter</span><span class="p">(</span><span class="s2">"a11y"</span><span class="p">)()</span>
<span class="n">generate</span> <span class="n">the</span> <span class="n">default</span> <span class="err">`</span><span class="n">resources</span><span class="err">`</span> <span class="nb">object</span> <span class="k">for</span> <span class="n">templating</span> <span class="n">the</span> <span class="n">notebook</span>
<span class="n">_</span><span class="p">,</span> <span class="n">resources</span> <span class="o">=</span> <span class="n">exporter</span><span class="o">.</span><span class="n">from_notebook_node</span><span class="p">(</span><span class="n">nbformat</span><span class="o">.</span><span class="n">reads</span><span class="p">(</span><span class="s2">"""{"cells": [], "metadata": </span><span class="si">{}</span><span class="s2">}"""</span><span class="p">,</span> <span class="mi">4</span><span class="p">))</span>
<span class="n">ours</span><span class="p">,</span><span class="n">theirs</span> <span class="o">=</span> <span class="n">Series</span><span class="p">()</span><span class="o">.</span><span class="n">template</span><span class="o">.</span><span class="n">environment</span><span class="p">,</span><span class="n">exporter</span><span class="o">.</span><span class="n">environment</span>
<span class="n">ours</span><span class="o">.</span><span class="n">loader</span> <span class="o">=</span> <span class="n">theirs</span><span class="o">.</span><span class="n">loader</span>
<span class="n">ours</span><span class="o">.</span><span class="n">filters</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="o">**</span><span class="n">theirs</span><span class="o">.</span><span class="n">filters</span><span class="p">,</span> <span class="o">**</span><span class="n">ours</span><span class="o">.</span><span class="n">filters</span><span class="p">})</span>
<span class="n">ours</span><span class="o">.</span><span class="n">globals</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="o">**</span><span class="n">theirs</span><span class="o">.</span><span class="n">globals</span><span class="p">,</span> <span class="o">**</span><span class="n">ours</span><span class="o">.</span><span class="n">globals</span><span class="p">})</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-16-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-16" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-16-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{'jupyter': {'source_hidden': True}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-16-loc" role="none">
12
</td>
<td class="nb-outputs" role="none">
<span class="nb-outputs visually-hidden" id="cell-16-outputs-len">
1 outputs.
</span>
<fieldset class="nb-outputs" data-outputs="1">
<legend aria-describedby="nb-outputs-desc" hidden id="cell-16-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
13
</span>
<span aria-hidden="true">
]
</span>
</legend>
<p>
merge our default async jinja exporter with the legacy nbconvert importer. the async template is a massive improvement of the sync nbconvert.
</p>
<pre><code>exporter = nbconvert.get_exporter("a11y")()
</code></pre>
<p>
generate the default
<code>
resources
</code>
object for templating the notebook
</p>
<pre><code>_, resources = exporter.from_notebook_node(nbformat.reads("""{"cells": [], "metadata": {}}""", 4))
ours,theirs = Series().template.environment,exporter.environment
ours.loader = theirs.loader
ours.filters.update({**theirs.filters, **ours.filters})
ours.globals.update({**theirs.globals, **ours.globals})
</code></pre>
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 17 cell-17-cell_type" class="cell code" data-index="17" data-loc="11" role="listitem">
<th class="nb-anchor" role="none" scope="row">
<a aria-describedby="nb-code-label nb-cell-label cell-17-loc nb-loc-label
cell-17-outputs-len" aria-labelledby="nb-cell-label 17" class="nb-anchor visually-hidden" href="#17" id="17">
17
</a>
</th>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-17-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
14
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label id="cell-17-cell_type">
code
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-17-source-label" class="nb-toolbar" hidden id="cell-17" name="cell-17">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-17-start" role="none">
</td>
<td class="nb-end" hidden id="cell-17-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-17-source-label nb-source-label" class="nb-source" form="cell-17" id="cell-17-source" name="source" readonly rows="11">%%
create the footer for all of the pages. currently there is no specific license identified,
but if there were we should use the [license microformat](https://microformats.org/wiki/rel-license).
footer = ours.filters["markdown"](F"""
By {config.author}
© Copyright {config.copyright}
""")
footer</textarea>
<div aria-labelledby="nb-source-label" aria-roledescription="highlighted" role="group">
<div class="highlight">
<pre><span></span><code><span class="o">%%</span>
<span class="n">create</span> <span class="n">the</span> <span class="n">footer</span> <span class="k">for</span> <span class="nb">all</span> <span class="n">of</span> <span class="n">the</span> <span class="n">pages</span><span class="o">.</span> <span class="n">currently</span> <span class="n">there</span> <span class="ow">is</span> <span class="n">no</span> <span class="n">specific</span> <span class="n">license</span> <span class="n">identified</span><span class="p">,</span>
<span class="n">but</span> <span class="k">if</span> <span class="n">there</span> <span class="n">were</span> <span class="n">we</span> <span class="n">should</span> <span class="n">use</span> <span class="n">the</span> <span class="p">[</span><span class="n">license</span> <span class="n">microformat</span><span class="p">](</span><span class="n">https</span><span class="p">:</span><span class="o">//</span><span class="n">microformats</span><span class="o">.</span><span class="n">org</span><span class="o">/</span><span class="n">wiki</span><span class="o">/</span><span class="n">rel</span><span class="o">-</span><span class="n">license</span><span class="p">)</span><span class="o">.</span>
<span class="n">footer</span> <span class="o">=</span> <span class="n">ours</span><span class="o">.</span><span class="n">filters</span><span class="p">[</span><span class="s2">"markdown"</span><span class="p">](</span><span class="sa">F</span><span class="s2">"""</span>
<span class="s2">By </span><span class="si">{</span><span class="n">config</span><span class="o">.</span><span class="n">author</span><span class="si">}</span>
<span class="s2"> </span>
<span class="s2">© Copyright </span><span class="si">{</span><span class="n">config</span><span class="o">.</span><span class="n">copyright</span><span class="si">}</span>
<span class="s2"> """</span><span class="p">)</span>
<span class="n">footer</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-17-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-17" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-17-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{'jupyter': {'source_hidden': True}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-17-loc" role="none">
11
</td>
<td class="nb-outputs" role="none">
<span class="nb-outputs visually-hidden" id="cell-17-outputs-len">
2 outputs.
</span>
<fieldset class="nb-outputs" data-outputs="2">
<legend aria-describedby="nb-outputs-desc" hidden id="cell-17-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
14
</span>
<span aria-hidden="true">
]
</span>
</legend>
<p>
create the footer for all of the pages. currently there is no specific license identified,
but if there were we should use the
<a href="https://microformats.org/wiki/rel-license">
license microformat
</a>
.
</p>
<pre><code>footer = ours.filters["markdown"](F"""
</code></pre>
<p>
By {config.author}
</p>
<p>
© Copyright {config.copyright}
</p>
<pre><code>""")
footer
</code></pre>
<pre class="plain">'<p>By STScI</p>\n<p>© Copyright 2022-2024</p>\n'</pre>
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 18 cell-18-cell_type" class="cell code" data-index="18" data-loc="4" role="listitem">
<th class="nb-anchor" role="none" scope="row">
<a aria-describedby="nb-code-label nb-cell-label cell-18-loc nb-loc-label
cell-18-outputs-len" aria-labelledby="nb-cell-label 18" class="nb-anchor visually-hidden" href="#18" id="18">
18
</a>
</th>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-18-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
15
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label id="cell-18-cell_type">
code
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-18-source-label" class="nb-toolbar" hidden id="cell-18" name="cell-18">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-18-start" role="none">
</td>
<td class="nb-end" hidden id="cell-18-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-18-source-label nb-source-label" class="nb-source" form="cell-18" id="cell-18-source" name="source" readonly rows="4">%%
add site navigation in the post processing step
previous and next still needs to be added previous and next is relative.
we'll want the config to do this work too. it needs to be passed to the template to construct license and link information
</textarea>
<div aria-labelledby="nb-source-label" aria-roledescription="highlighted" role="group">
<div class="highlight">
<pre><span></span><code><span class="o">%%</span>
<span class="n">add</span> <span class="n">site</span> <span class="n">navigation</span> <span class="ow">in</span> <span class="n">the</span> <span class="n">post</span> <span class="n">processing</span> <span class="n">step</span>
<span class="n">previous</span> <span class="ow">and</span> <span class="nb">next</span> <span class="n">still</span> <span class="n">needs</span> <span class="n">to</span> <span class="n">be</span> <span class="n">added</span> <span class="n">previous</span> <span class="ow">and</span> <span class="nb">next</span> <span class="ow">is</span> <span class="n">relative</span><span class="o">.</span>
<span class="n">we</span><span class="s1">'ll want the config to do this work too. it needs to be passed to the template to construct license and link information</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-18-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-18" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-18-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{'jupyter': {'source_hidden': True}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-18-loc" role="none">
4
</td>
<td class="nb-outputs" role="none">
<span class="nb-outputs visually-hidden" id="cell-18-outputs-len">
1 outputs.
</span>
<fieldset class="nb-outputs" data-outputs="1">
<legend aria-describedby="nb-outputs-desc" hidden id="cell-18-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
15
</span>
<span aria-hidden="true">
]
</span>
</legend>
<p>
add site navigation in the post processing step
previous and next still needs to be added previous and next is relative.
we'll want the config to do this work too. it needs to be passed to the template to construct license and link information
</p>
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 19 cell-19-cell_type" class="cell code" data-index="19" data-loc="7" role="listitem">
<th class="nb-anchor" role="none" scope="row">
<a aria-describedby="nb-code-label nb-cell-label cell-19-loc nb-loc-label
cell-19-outputs-len" aria-labelledby="nb-cell-label 19" class="nb-anchor visually-hidden" href="#19" id="19">
19
</a>
</th>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-19-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
16
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label id="cell-19-cell_type">
code
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-19-source-label" class="nb-toolbar" hidden id="cell-19" name="cell-19">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-19-start" role="none">
</td>
<td class="nb-end" hidden id="cell-19-end" role="none">
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-19-source-label nb-source-label" class="nb-source" form="cell-19" id="cell-19-source" name="source" readonly rows="7"> htmls = (
await notebooks.head().template.render_template(
"a11y/table.html.j2", resources=resources, config=config,
footer=footer
)
).apply(exporter.post_process_html)
htmls.to_frame().T</textarea>
<div aria-labelledby="nb-source-label" aria-roledescription="highlighted" role="group">
<div class="highlight">
<pre><span></span><code> <span class="n">htmls</span> <span class="o">=</span> <span class="p">(</span>
<span class="k">await</span> <span class="n">notebooks</span><span class="o">.</span><span class="n">head</span><span class="p">()</span><span class="o">.</span><span class="n">template</span><span class="o">.</span><span class="n">render_template</span><span class="p">(</span>
<span class="s2">"a11y/table.html.j2"</span><span class="p">,</span> <span class="n">resources</span><span class="o">=</span><span class="n">resources</span><span class="p">,</span> <span class="n">config</span><span class="o">=</span><span class="n">config</span><span class="p">,</span>
<span class="n">footer</span><span class="o">=</span><span class="n">footer</span>
<span class="p">)</span>
<span class="p">)</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">exporter</span><span class="o">.</span><span class="n">post_process_html</span><span class="p">)</span>
<span class="n">htmls</span><span class="o">.</span><span class="n">to_frame</span><span class="p">()</span><span class="o">.</span><span class="n">T</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-19-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-19" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-19-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-19-loc" role="none">
7
</td>
<td class="nb-outputs" role="none">
<span class="nb-outputs visually-hidden" id="cell-19-outputs-len">
1 outputs.
</span>
<fieldset class="nb-outputs" data-outputs="1">
<legend aria-describedby="nb-outputs-desc" hidden id="cell-19-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
16
</span>
<span aria-hidden="true">
]
</span>
</legend>
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th>
file
</th>
<th>
mast_notebooks/notebooks/astrocut/making_tess_cubes_and_cutouts/making_tess_cubes_and_cutouts.ipynb
</th>
<th>
mast_notebooks/notebooks/astroquery/beginner_search/beginner_search.ipynb
</th>
<th>
mast_notebooks/notebooks/astroquery/beginner_zcut/beginner_zcut.ipynb
</th>
<th>
mast_notebooks/notebooks/astroquery/large_downloads/large_downloads.ipynb
</th>
<th>
mast_notebooks/notebooks/astroquery/historic_quasar_observations/historic_quasar_observations.ipynb
</th>
</tr>
</thead>
<tbody>
<tr>
<th>
0
</th>
<td>
<!DOCTYPE html>\n<html lang="en">\n <head>\n ...
</td>
<td>
<!DOCTYPE html>\n<html lang="en">\n <head>\n ...
</td>
<td>
<!DOCTYPE html>\n<html lang="en">\n <head>\n ...
</td>
<td>
<!DOCTYPE html>\n<html lang="en">\n <head>\n ...
</td>
<td>
<!DOCTYPE html>\n<html lang="en">\n <head>\n ...
</td>
</tr>
</tbody>
</table>
</div>
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 20 cell-20-cell_type" class="cell markdown" data-index="20" data-loc="1" role="listitem">
<th class="nb-anchor" role="none" scope="row">
<a aria-describedby="nb-markdown-label nb-cell-label " aria-labelledby="nb-cell-label 20" class="nb-anchor visually-hidden" href="#20" id="20">
20
</a>
</th>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-20-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label id="cell-20-cell_type">
markdown
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-20-source-label" class="nb-toolbar" hidden id="cell-20" name="cell-20">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-20-start" role="none">
</td>
<td class="nb-end" hidden id="cell-20-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-20-source-label nb-source-label" class="nb-source" form="cell-20" id="cell-20-source" name="source" readonly rows="1">view the generated documentation as inline iframes</textarea>
<div aria-labelledby="nb-source-label" aria-roledescription="highlighted" role="group">
<div class="highlight">
<pre><span></span><code>view the generated documentation as inline iframes
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-20-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-20" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-20-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{'data': {'text/html': '<p>view the generated documentation as inline iframes</p>\n'}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-20-loc" role="none">
1
</td>
<td class="nb-outputs" role="none">
<p>
view the generated documentation as inline iframes
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 21 cell-21-cell_type" class="cell code" data-index="21" data-loc="3" role="listitem">
<th class="nb-anchor" role="none" scope="row">
<a aria-describedby="nb-code-label nb-cell-label cell-21-loc nb-loc-label
cell-21-outputs-len" aria-labelledby="nb-cell-label 21" class="nb-anchor visually-hidden" href="#21" id="21">
21
</a>
</th>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-21-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
18
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label id="cell-21-cell_type">
code
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-21-source-label" class="nb-toolbar" hidden id="cell-21" name="cell-21">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-21-start" role="none">
</td>
<td class="nb-end" hidden id="cell-21-end" role="none">
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-21-source-label nb-source-label" class="nb-source" form="cell-21" id="cell-21-source" name="source" readonly rows="3"> iframe = """<iframe height="600" srcdoc="{}" width="100%"></iframe>"""
iframes = htmls.apply(compose_left(__import__("html").escape, iframe.format))
display(*iframes.apply(HTML))</textarea>
<div aria-labelledby="nb-source-label" aria-roledescription="highlighted" role="group">
<div class="highlight">
<pre><span></span><code> <span class="n">iframe</span> <span class="o">=</span> <span class="s2">"""<iframe height=600 width="100%" srcdoc="</span><span class="si">{}</span><span class="s2">"></iframe>"""</span>
<span class="n">iframes</span> <span class="o">=</span> <span class="n">htmls</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">compose_left</span><span class="p">(</span><span class="nb">__import__</span><span class="p">(</span><span class="s2">"html"</span><span class="p">)</span><span class="o">.</span><span class="n">escape</span><span class="p">,</span> <span class="n">iframe</span><span class="o">.</span><span class="n">format</span><span class="p">))</span>
<span class="n">display</span><span class="p">(</span><span class="o">*</span><span class="n">iframes</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">HTML</span><span class="p">))</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-21-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-21" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-21-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-21-loc" role="none">
3
</td>
<td class="nb-outputs" role="none">
<span class="nb-outputs visually-hidden" id="cell-21-outputs-len">
5 outputs.
</span>
<fieldset class="nb-outputs" data-outputs="5">
<legend aria-describedby="nb-outputs-desc" hidden id="cell-21-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
18
</span>
<span aria-hidden="true">
]
</span>
</legend>
<iframe height="600" srcdoc="<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta content="width=device-width, initial-scale=1.0" name="viewport">
<title>
Notebook
</title>
<meta content="light" name="color-scheme">
<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js">
</script>
<style type="text/css">
/* # accessible css configurations
we define these high level variables to allow for configuration during use and testing.
we are not sure what the preferred default values are for these any accessible notebooks features.
the variables specify critical degrees of freedom in the notebook style.
## notebook interface specific css variables
*/
:root {
--nb-focus-width: 3px;
--nb-accent-color: auto;
--nb-background-color-dark: #2b2a33;
--nb-background-color-light: #FFFFFF;
--nb-margin: 5%;
--nb-font-size: 16px;
--nb-font-family: serif;
--nb-line-height: 1.5;
}
/* map css variables to css properties on the `body` elements.
this mapping turns out css variables into user interfaces. */
body {
font-size: var(--nb-font-size);
font-family: var(--nb-font-family);
accent-color: var(--nb-accent-color);
margin-left: var(--nb-margin);
margin-right: var(--nb-margin);
line-height: var(--nb-line-height);
width: calc(100% - 2*var(--nb-margin));
}
/* ## notebook settings
the notebook settings interface connects configurable css features to the user.
we use dialog elements to represents an element that should be hidden by default.
it turns out there are two ways to layout `dialog` elements.
they can be used as modals or block elements.
we extract this dual purpose to provide both configurations to the user.
*/
dialog form>* {
display: block;
}
#nb-dialogs details[open]~dialog {
position: relative;
}
/* ## wcag accessibility guidelines as a feature
flexibility is the future of assistive interfaces.
it is not in the purvey of privileged developers and inaccessible systems to decide what priority accessibility is.
it is the fucking priority.
in this implementation, we want to satisfy broad user needs by making a lot of features configurable.
one configurable feature is the accessibility priority.
experienced developers may need less assistive features as they spend more time with an interface.
### buttons sizes
WCAG 2.1 defines a huge stinking AAA hit size for buttons. its beautiful.
/* satisfy AAA 2.5.5 */
.wcag-aaa button,
.wcag-aaa input[type=checkbox] {
min-height: 44px;
min-width: 44px;
}
.wcag-aaa a {
font-size: max(44px, var(--nb-font-size));
}
/* WCAG 2.2 clarified a AA button size that is used to style checkboxes and buttons. */
/* satisfy AA 2.5.8 minimum target requirement */
.wcag-aa button,
.wcag-aa input[type=checkbox] {
min-height: 24px;
min-width: 24px;
}
.wcag-aa a {
font-size: max(24px, var(--nb-font-size));
}
/* ### native textareas for AAA priority.
our AAA priority choices often ensure that third party libraries can not compromise the accessibility of notebook.
the design decisions are focused native elements so that the assistive technology users have consistent expectations and outcomes.
*/
.markdown textarea[name=source]+[role=group],
.wcag-a textarea[name=source],
.wcag-aa textarea[name=source],
.wcag-aaa .markdown textarea[name=source],
/* this group selector is very ambiguous */
.wcag-aaa textarea[name=source]+[role=group] {
display: none;
}
/* this is only needed for the lsit template variant */
ol#cells {
margin: unset;
padding: unset;
}
li.cell {
list-style-type: none;
}
/* ## notebook components layout */
td[role="none"]:not([hidden]) {
display: unset;
}
table {
border-spacing: unset;
}
main>.cell,
#cells .cell,
#cells > tbody {
display: flex;
flex-direction: column;
}
ol[reversed]#cells,
[reversed]#cells tbody {
flex-direction: column-reverse;
}
/* hide the visual output area when there are no outputs.
assistive technologies will recieve an audible notification that there are not outputs. */
fieldset[data-outputs="0"] {
display: none;
}
/* ## modifications to html defaults
### focus
there nothing to be said about this topic that [sara soueidan](https://www.sarasoueidan.com/blog/focus-indicators/) hasn't said.
we start with her [universal focus recommendation](https://www.sarasoueidan.com/blog/focus-indicators/#a-%E2%80%98universal%E2%80%99-focus-indicator).
*/
.cell:focus-within,
:focus-visible {
outline: max(var(--nb-focus-width), 1px) solid;
box-shadow: 0 0 0 calc(2 * max(var(--nb-focus-width), 1px));
}
/* on firefox, the input and output become interactive when there is overflow. chrome fixed this recently find reference.*/
textarea[name=source],
.cell>td>details {
overflow: auto;
min-width: 0;
}
#nb-settings li::marker,
summary[inert]::marker {
content: "";
}
/* ### inheriting styles */
input,
select,
button {
font-family: inherit;
font-size: inherit;
}
textarea {
font-family: monospace;
font-size: inherit;
line-height: inherit;
overflow: auto;
color: unset;
}
textarea[name=source] {
box-sizing: border-box;
width: 100%;
resize: vertical;
}
/* align checkboxes with buttons */
input[type="checkbox"] {
vertical-align: middle;
}
/* ## custom class selectors */
#nb-dialogs details:not([open])~dialog:not([open]):not(:focus-within):not(:active),
/* legend:not(:focus-within):not(:active), */
details.log:not([open])+table,
.visually-hidden:not(:focus-within):not(:active) {
clip: rect(0 0 0 0);
clip-path: inset(50%);
height: 1px;
overflow: hidden;
position: absolute;
white-space: nowrap;
width: 1px;
}
</style>
<style id="nb-light-theme" media="screen" type="text/css">
/** accessible pygments github-light-high-contrast generated by __main__**/
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
.hll { background-color: #0969da4a }
.c { color: #66707b } /* Comment */
.err { color: #a0111f } /* Error */
.k { color: #a0111f } /* Keyword */
.l { color: #702c00 } /* Literal */
.n { color: #622cbc } /* Name */
.o { color: #024c1a } /* Operator */
.p { color: #24292f } /* Punctuation */
.ch { color: #66707b } /* Comment.Hashbang */
.cm { color: #66707b } /* Comment.Multiline */
.cp { color: #66707b } /* Comment.Preproc */
.cpf { color: #66707b } /* Comment.PreprocFile */
.c1 { color: #66707b } /* Comment.Single */
.cs { color: #66707b } /* Comment.Special */
.gd { color: #023b95 } /* Generic.Deleted */
.ge { font-style: italic } /* Generic.Emph */
.gr { color: #a0111f } /* Generic.Error */
.gh { color: #023b95 } /* Generic.Heading */
.gs { font-weight: bold } /* Generic.Strong */
.gu { color: #023b95 } /* Generic.Subheading */
.kc { color: #023b95 } /* Keyword.Constant */
.kd { color: #a0111f } /* Keyword.Declaration */
.kn { color: #a0111f } /* Keyword.Namespace */
.kp { color: #a0111f } /* Keyword.Pseudo */
.kr { color: #a0111f } /* Keyword.Reserved */
.kt { color: #a0111f } /* Keyword.Type */
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.m { color: #702c00 } /* Literal.Number */
.s { color: #023b95 } /* Literal.String */
.na { color: #702c00 } /* Name.Attribute */
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.nd { color: #702c00 } /* Name.Decorator */
.ni { color: #024c1a } /* Name.Entity */
.ne { color: #622cbc } /* Name.Exception */
.nf { color: #023b95 } /* Name.Function */
.nl { color: #702c00 } /* Name.Label */
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.nx { color: #622cbc } /* Name.Other */
.py { color: #023b95 } /* Name.Property */
.nt { color: #024c1a } /* Name.Tag */
.nv { color: #702c00 } /* Name.Variable */
.ow { color: #622cbc } /* Operator.Word */
.pm { color: #24292f } /* Punctuation.Marker */
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.dl { color: #023b95 } /* Literal.String.Delimiter */
.sd { color: #023b95 } /* Literal.String.Doc */
.s2 { color: #023b95 } /* Literal.String.Double */
.se { color: #023b95 } /* Literal.String.Escape */
.sh { color: #023b95 } /* Literal.String.Heredoc */
.si { color: #023b95 } /* Literal.String.Interpol */
.sx { color: #023b95 } /* Literal.String.Other */
.sr { color: #023b95 } /* Literal.String.Regex */
.s1 { color: #023b95 } /* Literal.String.Single */
.ss { color: #023b95 } /* Literal.String.Symbol */
.bp { color: #702c00 } /* Name.Builtin.Pseudo */
.fm { color: #023b95 } /* Name.Function.Magic */
.vc { color: #702c00 } /* Name.Variable.Class */
.vg { color: #702c00 } /* Name.Variable.Global */
.vi { color: #702c00 } /* Name.Variable.Instance */
.vm { color: #702c00 } /* Name.Variable.Magic */
.il { color: #702c00 } /* Literal.Number.Integer.Long */
</style>
<style id="nb-dark-theme" media="not screen" type="text/css">
/** accessible pygments github-dark-high-contrast generated by __main__**/
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
.hll { background-color: #58a6ff70 }
.c { color: #d9dee3 } /* Comment */
.err { color: #ff9492 } /* Error */
.k { color: #ff9492 } /* Keyword */
.l { color: #ffb757 } /* Literal */
.n { color: #dbb7ff } /* Name */
.o { color: #72f088 } /* Operator */
.p { color: #C9D1D9 } /* Punctuation */
.ch { color: #d9dee3 } /* Comment.Hashbang */
.cm { color: #d9dee3 } /* Comment.Multiline */
.cp { color: #d9dee3 } /* Comment.Preproc */
.cpf { color: #d9dee3 } /* Comment.PreprocFile */
.c1 { color: #d9dee3 } /* Comment.Single */
.cs { color: #d9dee3 } /* Comment.Special */
.gd { color: #91cbff } /* Generic.Deleted */
.ge { font-style: italic } /* Generic.Emph */
.gr { color: #ff9492 } /* Generic.Error */
.gh { color: #91cbff } /* Generic.Heading */
.gs { font-weight: bold } /* Generic.Strong */
.gu { color: #91cbff } /* Generic.Subheading */
.kc { color: #91cbff } /* Keyword.Constant */
.kd { color: #ff9492 } /* Keyword.Declaration */
.kn { color: #ff9492 } /* Keyword.Namespace */
.kp { color: #ff9492 } /* Keyword.Pseudo */
.kr { color: #ff9492 } /* Keyword.Reserved */
.kt { color: #ff9492 } /* Keyword.Type */
.ld { color: #ffb757 } /* Literal.Date */
.m { color: #ffb757 } /* Literal.Number */
.s { color: #91cbff } /* Literal.String */
.na { color: #ffb757 } /* Name.Attribute */
.nb { color: #ffb757 } /* Name.Builtin */
.nc { color: #91cbff } /* Name.Class */
.no { color: #91cbff } /* Name.Constant */
.nd { color: #ffb757 } /* Name.Decorator */
.ni { color: #72f088 } /* Name.Entity */
.ne { color: #dbb7ff } /* Name.Exception */
.nf { color: #91cbff } /* Name.Function */
.nl { color: #ffb757 } /* Name.Label */
.nn { color: #C9D1D9 } /* Name.Namespace */
.nx { color: #dbb7ff } /* Name.Other */
.py { color: #91cbff } /* Name.Property */
.nt { color: #72f088 } /* Name.Tag */
.nv { color: #ffb757 } /* Name.Variable */
.ow { color: #dbb7ff } /* Operator.Word */
.pm { color: #C9D1D9 } /* Punctuation.Marker */
.w { color: #C9D1D9 } /* Text.Whitespace */
.mb { color: #ffb757 } /* Literal.Number.Bin */
.mf { color: #ffb757 } /* Literal.Number.Float */
.mh { color: #ffb757 } /* Literal.Number.Hex */
.mi { color: #ffb757 } /* Literal.Number.Integer */
.mo { color: #ffb757 } /* Literal.Number.Oct */
.sa { color: #91cbff } /* Literal.String.Affix */
.sb { color: #91cbff } /* Literal.String.Backtick */
.sc { color: #91cbff } /* Literal.String.Char */
.dl { color: #91cbff } /* Literal.String.Delimiter */
.sd { color: #91cbff } /* Literal.String.Doc */
.s2 { color: #91cbff } /* Literal.String.Double */
.se { color: #91cbff } /* Literal.String.Escape */
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<li>
<a href="#generating-cubes-and-cutouts-from-tess-ffis">
Generating Cubes and Cutouts from TESS FFIs
</a>
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<a href="#learning-goals">
Learning Goals
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Introduction
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Retrieving and Downloading the FFIs
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Retrieving the FFIs
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Downloading the FFIs
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<a href="#inspecting-the-ffis">
Inspecting the FFIs
</a>
</li>
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Creating a Cube
</a>
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Creating the Cutouts
</a>
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<a href="#exercise-generate-cutouts-for-tic-2527981-sector-27-spoc-products">
Exercise: Generate Cutouts for TIC 2527981 Sector 27 SPOC Products
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<textarea aria-labelledby="cell-1-source-label nb-source-label" class="nb-source" form="cell-1" id="cell-1-source" name="source" readonly rows="13">&lt;a id="top"&gt;&lt;/a&gt;
# Generating Cubes and Cutouts from TESS FFIs
***
## Learning Goals
By the end of this tutorial, you will:
- Learn about the two TESS full frame image (FFI) types: SPOC and TICA.
- Learn the structure of a TICA FFI.
- Learn how to retrieve and download TESS FFIs using `astroquery.mast.Observations`.
- Learn how to generate cubes from TESS FFIs with astrocut's `CubeFactory` and `TicaCubeFactory`.
- Learn how to generate cutouts from TESS cubes with astrocut's `CutoutFactory`.
- Learn the structure of a TESS target pixel file (TPF), and therefore an astrocut cutouts file, and how to retrieve and plot the data stored within.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>&lt;a id="top"&gt;&lt;/a&gt;
<span class="gh" title="Generic.Heading"># Generating Cubes and Cutouts from TESS FFIs</span>
***
<span class="gu" title="Generic.Subheading">## Learning Goals</span>
By the end of this tutorial, you will:
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>Learn about the two TESS full frame image (FFI) types: SPOC and TICA.
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>Learn the structure of a TICA FFI.
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>Learn how to retrieve and download TESS FFIs using <span class="sb" title="Literal.String.Backtick">`astroquery.mast.Observations`</span>.
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>Learn how to generate cubes from TESS FFIs with astrocut's <span class="sb" title="Literal.String.Backtick">`CubeFactory`</span> and <span class="sb" title="Literal.String.Backtick">`TicaCubeFactory`</span>.
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>Learn how to generate cutouts from TESS cubes with astrocut's <span class="sb" title="Literal.String.Backtick">`CutoutFactory`</span>.
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>Learn the structure of a TESS target pixel file (TPF), and therefore an astrocut cutouts file, and how to retrieve and plot the data stored within.
</code></pre>
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13
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<a id="top">
</a>
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<h1>
<a href="#generating-cubes-and-cutouts-from-tess-ffis">
Generating Cubes and Cutouts from TESS FFIs
</a>
</h1>
<hr>
<h2>
<a href="#learning-goals">
Learning Goals
</a>
</h2>
<p>
By the end of this tutorial, you will:
</p>
<ul>
<li>
Learn about the two TESS full frame image (FFI) types: SPOC and TICA.
</li>
<li>
Learn the structure of a TICA FFI.
</li>
<li>
Learn how to retrieve and download TESS FFIs using
<code>
astroquery.mast.Observations
</code>
.
</li>
<li>
Learn how to generate cubes from TESS FFIs with astrocut's
<code>
CubeFactory
</code>
and
<code>
TicaCubeFactory
</code>
.
</li>
<li>
Learn how to generate cutouts from TESS cubes with astrocut's
<code>
CutoutFactory
</code>
.
</li>
<li>
Learn the structure of a TESS target pixel file (TPF), and therefore an astrocut cutouts file, and how to retrieve and plot the data stored within.
</li>
</ul>
</td>
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<textarea aria-labelledby="cell-2-source-label nb-source-label" class="nb-source" form="cell-2" id="cell-2-source" name="source" readonly rows="19">## Introduction
In this tutorial, you will learn the basic functionality of [astrocut](https://github.com/spacetelescope/astrocut)'s `CubeFactory`, `TicaCubeFactory`, and `CutoutFactory`. These 3 tools allow users to generate cubes and cutouts of TESS images. TESS image cubes are stacks of TESS full frame images (TESS FFIs) which are formatted to allow users to make cutouts with `CutoutFactory`. These cutouts are sub-images that will only contain your region of interest, to make analysis easier.
The process of generating cubes, and making cutouts from them, mimicks the web-based [TESSCut](https://mast.stsci.edu/tesscut/) service. This tutorial will teach you to make cubes and cutouts that are customized for your specific use-cases, while also giving insight to how TESSCut calls upon astrocut to create cutouts.
As in the TESSCut service, there are _two_ different TESS FFI product types you can work with in this notebook. These two product types are the TESS mission-provided, [Science Processing Operations Center (SPOC) FFIs](https://archive.stsci.edu/missions-and-data/tess), and the [TESS Image CAlibrator (TICA) FFIs](https://archive.stsci.edu/hlsp/tica). Both are available via `astroquery.mast.Observations`, and both have different use-cases. The TICA products offer the latest sector observations due to their faster delivery cadence, and can be available up to 3 times sooner than their SPOC counterparts. As such, for those who are working with time-sensitive observations, TICA may be the best option for generating your cutouts. Note that the SPOC and TICA products are processed through different pipelines and thus have different pixel values and WCS solutions. Please refer to the [SPOC pipeline paper](https://ui.adsabs.harvard.edu/abs/2016SPIE.9913E..3EJ/abstract) and the [TICA pipeline paper](https://iopscience.iop.org/article/10.3847/2515-5172/abd63a) for more information on the differences and similarities between the SPOC and TICA products.

The workflow for this notebook consists of:
* [Retrieving and Downloading the FFIs](#Retrieving-and-Downloading-the-FFIs)
* [Retrieving the FFIs](#Retrieving-the-FFIs)
* [Downloading the FFIs](#Downloading-the-FFIs)
* [Inspecting the FFIs](#Inspecting-the-FFIs)
* [Creating a Cube](#Creating-a-Cube)
* [Creating the Cutouts](#Creating-the-Cutouts)
* [Exercise: Generate Cutouts for TIC 2527981 Sector 27 SPOC Products](#Exercise:-Generate-Cutouts-for-TIC-2527981-Sector-27-SPOC-Products)
* [Resources](#Resources)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## Introduction</span>
In this tutorial, you will learn the basic functionality of [<span class="nt" title="Name.Tag">astrocut</span>](<span class="na" title="Name.Attribute">https://github.com/spacetelescope/astrocut</span>)'s <span class="sb" title="Literal.String.Backtick">`CubeFactory`</span>, <span class="sb" title="Literal.String.Backtick">`TicaCubeFactory`</span>, and <span class="sb" title="Literal.String.Backtick">`CutoutFactory`</span>. These 3 tools allow users to generate cubes and cutouts of TESS images. TESS image cubes are stacks of TESS full frame images (TESS FFIs) which are formatted to allow users to make cutouts with <span class="sb" title="Literal.String.Backtick">`CutoutFactory`</span>. These cutouts are sub-images that will only contain your region of interest, to make analysis easier.
The process of generating cubes, and making cutouts from them, mimicks the web-based [<span class="nt" title="Name.Tag">TESSCut</span>](<span class="na" title="Name.Attribute">https://mast.stsci.edu/tesscut/</span>) service. This tutorial will teach you to make cubes and cutouts that are customized for your specific use-cases, while also giving insight to how TESSCut calls upon astrocut to create cutouts.
As in the TESSCut service, there are <span class="ge" title="Generic.Emph">_two_</span> different TESS FFI product types you can work with in this notebook. These two product types are the TESS mission-provided, [<span class="nt" title="Name.Tag">Science Processing Operations Center (SPOC) FFIs</span>](<span class="na" title="Name.Attribute">https://archive.stsci.edu/missions-and-data/tess</span>), and the [<span class="nt" title="Name.Tag">TESS Image CAlibrator (TICA) FFIs</span>](<span class="na" title="Name.Attribute">https://archive.stsci.edu/hlsp/tica</span>). Both are available via <span class="sb" title="Literal.String.Backtick">`astroquery.mast.Observations`</span>, and both have different use-cases. The TICA products offer the latest sector observations due to their faster delivery cadence, and can be available up to 3 times sooner than their SPOC counterparts. As such, for those who are working with time-sensitive observations, TICA may be the best option for generating your cutouts. Note that the SPOC and TICA products are processed through different pipelines and thus have different pixel values and WCS solutions. Please refer to the [<span class="nt" title="Name.Tag">SPOC pipeline paper</span>](<span class="na" title="Name.Attribute">https://ui.adsabs.harvard.edu/abs/2016SPIE.9913E..3EJ/abstract</span>) and the [<span class="nt" title="Name.Tag">TICA pipeline paper</span>](<span class="na" title="Name.Attribute">https://iopscience.iop.org/article/10.3847/2515-5172/abd63a</span>) for more information on the differences and similarities between the SPOC and TICA products.

The workflow for this notebook consists of:
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Retrieving and Downloading the FFIs</span>](<span class="na" title="Name.Attribute">#Retrieving-and-Downloading-the-FFIs</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Retrieving the FFIs</span>](<span class="na" title="Name.Attribute">#Retrieving-the-FFIs</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Downloading the FFIs</span>](<span class="na" title="Name.Attribute">#Downloading-the-FFIs</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Inspecting the FFIs</span>](<span class="na" title="Name.Attribute">#Inspecting-the-FFIs</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Creating a Cube</span>](<span class="na" title="Name.Attribute">#Creating-a-Cube</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Creating the Cutouts</span>](<span class="na" title="Name.Attribute">#Creating-the-Cutouts</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Exercise: Generate Cutouts for TIC 2527981 Sector 27 SPOC Products</span>](<span class="na" title="Name.Attribute">#Exercise:-Generate-Cutouts-for-TIC-2527981-Sector-27-SPOC-Products</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Resources</span>](<span class="na" title="Name.Attribute">#Resources</span>)
</code></pre>
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<h2>
<a href="#introduction">
Introduction
</a>
</h2>
<p>
In this tutorial, you will learn the basic functionality of
<a href="https://github.com/spacetelescope/astrocut">
astrocut
</a>
's
<code>
CubeFactory
</code>
,
<code>
TicaCubeFactory
</code>
, and
<code>
CutoutFactory
</code>
. These 3 tools allow users to generate cubes and cutouts of TESS images. TESS image cubes are stacks of TESS full frame images (TESS FFIs) which are formatted to allow users to make cutouts with
<code>
CutoutFactory
</code>
. These cutouts are sub-images that will only contain your region of interest, to make analysis easier.
</p>
<p>
The process of generating cubes, and making cutouts from them, mimicks the web-based
<a href="https://mast.stsci.edu/tesscut/">
TESSCut
</a>
service. This tutorial will teach you to make cubes and cutouts that are customized for your specific use-cases, while also giving insight to how TESSCut calls upon astrocut to create cutouts.
</p>
<p>
As in the TESSCut service, there are
<em>
two
</em>
different TESS FFI product types you can work with in this notebook. These two product types are the TESS mission-provided,
<a href="https://archive.stsci.edu/missions-and-data/tess">
Science Processing Operations Center (SPOC) FFIs
</a>
, and the
<a href="https://archive.stsci.edu/hlsp/tica">
TESS Image CAlibrator (TICA) FFIs
</a>
. Both are available via
<code>
astroquery.mast.Observations
</code>
, and both have different use-cases. The TICA products offer the latest sector observations due to their faster delivery cadence, and can be available up to 3 times sooner than their SPOC counterparts. As such, for those who are working with time-sensitive observations, TICA may be the best option for generating your cutouts. Note that the SPOC and TICA products are processed through different pipelines and thus have different pixel values and WCS solutions. Please refer to the
<a href="https://ui.adsabs.harvard.edu/abs/2016SPIE.9913E..3EJ/abstract">
SPOC pipeline paper
</a>
and the
<a href="https://iopscience.iop.org/article/10.3847/2515-5172/abd63a">
TICA pipeline paper
</a>
for more information on the differences and similarities between the SPOC and TICA products.
</p>
<p>
<img alt="TICA and SPOC ingest" src="./tica-spoc-ingest.png">
</p>
<p>
The workflow for this notebook consists of:
</p>
<ul>
<li>
<a href="#Retrieving-and-Downloading-the-FFIs">
Retrieving and Downloading the FFIs
</a>
<ul>
<li>
<a href="#Retrieving-the-FFIs">
Retrieving the FFIs
</a>
</li>
<li>
<a href="#Downloading-the-FFIs">
Downloading the FFIs
</a>
</li>
<li>
<a href="#Inspecting-the-FFIs">
Inspecting the FFIs
</a>
</li>
</ul>
</li>
<li>
<a href="#Creating-a-Cube">
Creating a Cube
</a>
</li>
<li>
<a href="#Creating-the-Cutouts">
Creating the Cutouts
</a>
</li>
<li>
<a href="#Exercise:-Generate-Cutouts-for-TIC-2527981-Sector-27-SPOC-Products">
Exercise: Generate Cutouts for TIC 2527981 Sector 27 SPOC Products
</a>
</li>
<li>
<a href="#Resources">
Resources
</a>
</li>
</ul>
</td>
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<textarea aria-labelledby="cell-3-source-label nb-source-label" class="nb-source" form="cell-3" id="cell-3-source" name="source" readonly rows="14">## Imports
Below is a list of the packages used throughout this notebook, and their use-cases. Please ensure you have the recommended version of these packages installed on the environment this notebook has been launched in. See the `requirements.txt` file for recommended package versions.
- *astropy.units* for unit conversion
- *matplotlib.pyplot* for plotting data
- *numpy* to handle array functions
- *astrocut CubeFactory* for generating cubes out of TESS-SPOC FFIs
- *astrocut TicaCubeFactory* for generating cubes out of TICA FFIs
- *astrocut CutoutFactory* for generating cutouts out of astrocut cubes
- *astropy.coordinates SkyCoord* for creating sky coordinate objects
- *astropy.io fits* for accessing FITS files
- *astroquery.mast Observations* for retrieving and downloading the TESS FFIs
- *matplotlib.path Path* to generate a drawing path for plotting purposes
- *matplotlib.patches PathPatch* to generate a patch that represents the CCD footprint for plotting purposes </textarea>
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<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## Imports</span>
Below is a list of the packages used throughout this notebook, and their use-cases. Please ensure you have the recommended version of these packages installed on the environment this notebook has been launched in. See the <span class="sb" title="Literal.String.Backtick">`requirements.txt`</span> file for recommended package versions.
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="ge" title="Generic.Emph">*astropy.units*</span> for unit conversion
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="ge" title="Generic.Emph">*matplotlib.pyplot*</span> for plotting data
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="ge" title="Generic.Emph">*numpy*</span> to handle array functions
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="ge" title="Generic.Emph">*astrocut CubeFactory*</span> for generating cubes out of TESS-SPOC FFIs
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="ge" title="Generic.Emph">*astrocut TicaCubeFactory*</span> for generating cubes out of TICA FFIs
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="ge" title="Generic.Emph">*astrocut CutoutFactory*</span> for generating cutouts out of astrocut cubes
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="ge" title="Generic.Emph">*astropy.coordinates SkyCoord*</span> for creating sky coordinate objects
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="ge" title="Generic.Emph">*astropy.io fits*</span> for accessing FITS files
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="ge" title="Generic.Emph">*astroquery.mast Observations*</span> for retrieving and downloading the TESS FFIs
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="ge" title="Generic.Emph">*matplotlib.path Path*</span> to generate a drawing path for plotting purposes
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="ge" title="Generic.Emph">*matplotlib.patches PathPatch*</span> to generate a patch that represents the CCD footprint for plotting purposes
</code></pre>
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<h2>
<a href="#imports">
Imports
</a>
</h2>
<p>
Below is a list of the packages used throughout this notebook, and their use-cases. Please ensure you have the recommended version of these packages installed on the environment this notebook has been launched in. See the
<code>
requirements.txt
</code>
file for recommended package versions.
</p>
<ul>
<li>
<em>
astropy.units
</em>
for unit conversion
</li>
<li>
<em>
matplotlib.pyplot
</em>
for plotting data
</li>
<li>
<em>
numpy
</em>
to handle array functions
</li>
<li>
<em>
astrocut CubeFactory
</em>
for generating cubes out of TESS-SPOC FFIs
</li>
<li>
<em>
astrocut TicaCubeFactory
</em>
for generating cubes out of TICA FFIs
</li>
<li>
<em>
astrocut CutoutFactory
</em>
for generating cutouts out of astrocut cubes
</li>
<li>
<em>
astropy.coordinates SkyCoord
</em>
for creating sky coordinate objects
</li>
<li>
<em>
<a href="http://astropy.io">
astropy.io
</a>
fits
</em>
for accessing FITS files
</li>
<li>
<em>
astroquery.mast Observations
</em>
for retrieving and downloading the TESS FFIs
</li>
<li>
<em>
matplotlib.path Path
</em>
to generate a drawing path for plotting purposes
</li>
<li>
<em>
matplotlib.patches PathPatch
</em>
to generate a patch that represents the CCD footprint for plotting purposes
</li>
</ul>
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<textarea aria-labelledby="cell-4-source-label nb-source-label" class="nb-source" form="cell-4" id="cell-4-source" name="source" readonly rows="12">%matplotlib inline
import astropy.units as u
import matplotlib.pyplot as plt
import numpy as np
from astrocut import CubeFactory, TicaCubeFactory, CutoutFactory
from astropy.coordinates import SkyCoord
from astropy.io import fits
from astroquery.mast import Observations
from matplotlib.path import Path
from matplotlib.patches import PathPatch</textarea>
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<pre><span></span><code><span class="o" title="Operator">%</span><span class="n" title="Name">matplotlib</span> <span class="n" title="Name">inline</span>
<span class="kn" title="Keyword.Namespace">import</span> <span class="nn" title="Name.Namespace">astropy.units</span> <span class="k" title="Keyword">as</span> <span class="nn" title="Name.Namespace">u</span>
<span class="kn" title="Keyword.Namespace">import</span> <span class="nn" title="Name.Namespace">matplotlib.pyplot</span> <span class="k" title="Keyword">as</span> <span class="nn" title="Name.Namespace">plt</span>
<span class="kn" title="Keyword.Namespace">import</span> <span class="nn" title="Name.Namespace">numpy</span> <span class="k" title="Keyword">as</span> <span class="nn" title="Name.Namespace">np</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astrocut</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">CubeFactory</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">TicaCubeFactory</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">CutoutFactory</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astropy.coordinates</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">SkyCoord</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astropy.io</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">fits</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astroquery.mast</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">Observations</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">matplotlib.path</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">Path</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">matplotlib.patches</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">PathPatch</span>
</code></pre>
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<textarea aria-labelledby="cell-5-source-label nb-source-label" class="nb-source" form="cell-5" id="cell-5-source" name="source" readonly rows="1">Below are the helper functions we will call for plotting purposes later in the notebook.</textarea>
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<pre><span></span><code>Below are the helper functions we will call for plotting purposes later in the notebook.
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Below are the helper functions we will call for plotting purposes later in the notebook.
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<textarea aria-labelledby="cell-6-source-label nb-source-label" class="nb-source" form="cell-6" id="cell-6-source" name="source" readonly rows="71">def convert_coords(ra, dec):
""" Wrap the input coordinates `ra` and `dec` around the upper limit of 180
degrees and convert to radians so that they may be plotted on the Aitoff canvas.
Parameter(s)
-----------
ra : str, int, or float
The Right Ascension your target lands on, in degrees. May be any object type that
can be converted to a float, such as a string, or integer.
dec : str, int, or float
The Declination your target lands on, in degrees. May be any object type that
can be converted to a float, such as a string, or integer.
Returns
-------
ra_rad : float
The Right Ascension of your target in radians.
dec_rad : float
The Declination of your target in radians.
"""
# Make a SkyCoord object out of the RA, Dec
c = SkyCoord(ra=float(ra)*u.degree, dec=float(dec)*u.degree, frame='icrs')
# The plotting only works when the coordinates are in radians.
# And because it's an aitoff projection, we can't
# go beyond 180 degrees, so let's wrap the RA vals around that upper limit.
ra_rad = c.ra.wrap_at(180 * u.deg).radian
dec_rad = c.dec.radian
return ra_rad, dec_rad
def plot_footprint(coords):
""" Plots a polygon of 4 vertices onto an aitoff projection on a matplotlib canvas.
Parameter(s)
-----------
coords : list or array of tuples
An iterable object (can be a list or array) of 5 tuples, each tuple containing the
RA and Dec (float objects) of a vertice of your footprint. There MUST be 5: 1 for each
vertice, and 1 to return to the starting point, as PathPatch works with a set of drawing
instructions, rather than a predetermined shape.
Returns
-------
ax : Matplotlib.pyplot figure object subplot
The subplot that contains the aitoff projection and footprint drawing.
"""
assert len(coords) == 5, 'We need 5 sets of coordinates. 1 for each vertice + 1 to return to the starting point.'
instructions = [Path.MOVETO, Path.LINETO, Path.LINETO, Path.LINETO, Path.CLOSEPOLY]
# Create the path patch using the `coords` list of tuples and the
# instructions from above
c = np.array(coords)
path = Path(c, instructions)
ppatch = PathPatch(path, edgecolor='k', facecolor='salmon')
# Create a matplotlib canvas
fig = plt.figure(figsize=(12,12))
# We will be using the `aitoff` projection for a globe canvas
ax = plt.subplot(111, projection='aitoff')
# Need grids!
ax.grid()
# Add the patch to your canvas
ax.add_patch(ppatch)
return ax</textarea>
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<pre><span></span><code><span class="k" title="Keyword">def</span> <span class="nf" title="Name.Function">convert_coords</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">ra</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">dec</span><span class="p" title="Punctuation">):</span>
<span class="w" title="Text.Whitespace"> </span><span class="sd" title="Literal.String.Doc">""" Wrap the input coordinates `ra` and `dec` around the upper limit of 180</span>
<span class="sd" title="Literal.String.Doc"> degrees and convert to radians so that they may be plotted on the Aitoff canvas.</span>
<span class="sd" title="Literal.String.Doc"> </span>
<span class="sd" title="Literal.String.Doc"> Parameter(s)</span>
<span class="sd" title="Literal.String.Doc"> -----------</span>
<span class="sd" title="Literal.String.Doc"> ra : str, int, or float</span>
<span class="sd" title="Literal.String.Doc"> The Right Ascension your target lands on, in degrees. May be any object type that </span>
<span class="sd" title="Literal.String.Doc"> can be converted to a float, such as a string, or integer.</span>
<span class="sd" title="Literal.String.Doc"> dec : str, int, or float</span>
<span class="sd" title="Literal.String.Doc"> The Declination your target lands on, in degrees. May be any object type that </span>
<span class="sd" title="Literal.String.Doc"> can be converted to a float, such as a string, or integer.</span>
<span class="sd" title="Literal.String.Doc"> </span>
<span class="sd" title="Literal.String.Doc"> Returns</span>
<span class="sd" title="Literal.String.Doc"> -------</span>
<span class="sd" title="Literal.String.Doc"> ra_rad : float</span>
<span class="sd" title="Literal.String.Doc"> The Right Ascension of your target in radians.</span>
<span class="sd" title="Literal.String.Doc"> dec_rad : float</span>
<span class="sd" title="Literal.String.Doc"> The Declination of your target in radians.</span>
<span class="sd" title="Literal.String.Doc"> """</span>
<span class="c1" title="Comment.Single"># Make a SkyCoord object out of the RA, Dec</span>
<span class="n" title="Name">c</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">SkyCoord</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">ra</span><span class="o" title="Operator">=</span><span class="nb" title="Name.Builtin">float</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">ra</span><span class="p" title="Punctuation">)</span><span class="o" title="Operator">*</span><span class="n" title="Name">u</span><span class="o" title="Operator">.</span><span class="n" title="Name">degree</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">dec</span><span class="o" title="Operator">=</span><span class="nb" title="Name.Builtin">float</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">dec</span><span class="p" title="Punctuation">)</span><span class="o" title="Operator">*</span><span class="n" title="Name">u</span><span class="o" title="Operator">.</span><span class="n" title="Name">degree</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">frame</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'icrs'</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># The plotting only works when the coordinates are in radians.</span>
<span class="c1" title="Comment.Single"># And because it's an aitoff projection, we can't </span>
<span class="c1" title="Comment.Single"># go beyond 180 degrees, so let's wrap the RA vals around that upper limit.</span>
<span class="n" title="Name">ra_rad</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">c</span><span class="o" title="Operator">.</span><span class="n" title="Name">ra</span><span class="o" title="Operator">.</span><span class="n" title="Name">wrap_at</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">180</span> <span class="o" title="Operator">*</span> <span class="n" title="Name">u</span><span class="o" title="Operator">.</span><span class="n" title="Name">deg</span><span class="p" title="Punctuation">)</span><span class="o" title="Operator">.</span><span class="n" title="Name">radian</span>
<span class="n" title="Name">dec_rad</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">c</span><span class="o" title="Operator">.</span><span class="n" title="Name">dec</span><span class="o" title="Operator">.</span><span class="n" title="Name">radian</span>
<span class="k" title="Keyword">return</span> <span class="n" title="Name">ra_rad</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">dec_rad</span>
<span class="k" title="Keyword">def</span> <span class="nf" title="Name.Function">plot_footprint</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">coords</span><span class="p" title="Punctuation">):</span>
<span class="w" title="Text.Whitespace"> </span><span class="sd" title="Literal.String.Doc">""" Plots a polygon of 4 vertices onto an aitoff projection on a matplotlib canvas.</span>
<span class="sd" title="Literal.String.Doc"> </span>
<span class="sd" title="Literal.String.Doc"> Parameter(s)</span>
<span class="sd" title="Literal.String.Doc"> -----------</span>
<span class="sd" title="Literal.String.Doc"> coords : list or array of tuples</span>
<span class="sd" title="Literal.String.Doc"> An iterable object (can be a list or array) of 5 tuples, each tuple containing the </span>
<span class="sd" title="Literal.String.Doc"> RA and Dec (float objects) of a vertice of your footprint. There MUST be 5: 1 for each</span>
<span class="sd" title="Literal.String.Doc"> vertice, and 1 to return to the starting point, as PathPatch works with a set of drawing </span>
<span class="sd" title="Literal.String.Doc"> instructions, rather than a predetermined shape.</span>
<span class="sd" title="Literal.String.Doc"> </span>
<span class="sd" title="Literal.String.Doc"> Returns</span>
<span class="sd" title="Literal.String.Doc"> -------</span>
<span class="sd" title="Literal.String.Doc"> ax : Matplotlib.pyplot figure object subplot</span>
<span class="sd" title="Literal.String.Doc"> The subplot that contains the aitoff projection and footprint drawing.</span>
<span class="sd" title="Literal.String.Doc"> """</span>
<span class="k" title="Keyword">assert</span> <span class="nb" title="Name.Builtin">len</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">coords</span><span class="p" title="Punctuation">)</span> <span class="o" title="Operator">==</span> <span class="mi" title="Literal.Number.Integer">5</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'We need 5 sets of coordinates. 1 for each vertice + 1 to return to the starting point.'</span>
<span class="n" title="Name">instructions</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="n" title="Name">Path</span><span class="o" title="Operator">.</span><span class="n" title="Name">MOVETO</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">Path</span><span class="o" title="Operator">.</span><span class="n" title="Name">LINETO</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">Path</span><span class="o" title="Operator">.</span><span class="n" title="Name">LINETO</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">Path</span><span class="o" title="Operator">.</span><span class="n" title="Name">LINETO</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">Path</span><span class="o" title="Operator">.</span><span class="n" title="Name">CLOSEPOLY</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># Create the path patch using the `coords` list of tuples and the</span>
<span class="c1" title="Comment.Single"># instructions from above</span>
<span class="n" title="Name">c</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">array</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">coords</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">path</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Path</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">c</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">instructions</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ppatch</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">PathPatch</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">path</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">edgecolor</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'k'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">facecolor</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'salmon'</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Create a matplotlib canvas</span>
<span class="n" title="Name">fig</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">figure</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">figsize</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">12</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">12</span><span class="p" title="Punctuation">))</span>
<span class="c1" title="Comment.Single"># We will be using the `aitoff` projection for a globe canvas</span>
<span class="n" title="Name">ax</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">subplot</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">111</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">projection</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'aitoff'</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Need grids!</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">grid</span><span class="p" title="Punctuation">()</span>
<span class="c1" title="Comment.Single"># Add the patch to your canvas</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">add_patch</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">ppatch</span><span class="p" title="Punctuation">)</span>
<span class="k" title="Keyword">return</span> <span class="n" title="Name">ax</span>
</code></pre>
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<pre><span></span><code>***
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<textarea aria-labelledby="cell-8-source-label nb-source-label" class="nb-source" form="cell-8" id="cell-8-source" name="source" readonly rows="3">## Retrieving and Downloading the FFIs
Before we begin generating cubes and cutouts from TESS FFIs, we must first acquire these FFIs. To do this, we will request and download the FFIs locally using [astroquery.mast](https://astroquery.readthedocs.io/en/latest/mast/mast.html), or more specifically, the `astroquery.mast.Observations` tool, which will grant us access to the database that houses the TESS products. This is also how the TESSCut API generates its cutouts.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Retrieving and Downloading the FFIs</span>
Before we begin generating cubes and cutouts from TESS FFIs, we must first acquire these FFIs. To do this, we will request and download the FFIs locally using [<span class="nt" title="Name.Tag">astroquery.mast</span>](<span class="na" title="Name.Attribute">https://astroquery.readthedocs.io/en/latest/mast/mast.html</span>), or more specifically, the <span class="sb" title="Literal.String.Backtick">`astroquery.mast.Observations`</span> tool, which will grant us access to the database that houses the TESS products. This is also how the TESSCut API generates its cutouts.
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<h2>
<a href="#retrieving-and-downloading-the-ffis">
Retrieving and Downloading the FFIs
</a>
</h2>
<p>
Before we begin generating cubes and cutouts from TESS FFIs, we must first acquire these FFIs. To do this, we will request and download the FFIs locally using
<a href="https://astroquery.readthedocs.io/en/latest/mast/mast.html">
astroquery.mast
</a>
, or more specifically, the
<code>
astroquery.mast.Observations
</code>
tool, which will grant us access to the database that houses the TESS products. This is also how the TESSCut API generates its cutouts.
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### Retrieving the FFIs</span>
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Retrieving the FFIs
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<textarea aria-labelledby="cell-10-source-label nb-source-label" class="nb-source" form="cell-10" id="cell-10-source" name="source" readonly rows="3">You can download FFIs for any sector, camera, and CCD type. This notebook will generate cutouts for target TIC 2527981 using TICA FFIs in sector 27, but feel free to substitute a favorite target of your own. To find our target, we will filter with the database criteria: `coordinates`, `target_name`, `dataproduct_type`, and `sequence_number`.
The `coordinates` criteria will filter for the coordinates of our target, while the `target_name` will filter for TICA FFIs and exclude all SPOC FFIs. `dataproduct_type` ensures we get image observations, and the `sequence_number` will be used to filter for only sector 27 observations. With this information, let's compile our query...</textarea>
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<pre><span></span><code>You can download FFIs for any sector, camera, and CCD type. This notebook will generate cutouts for target TIC 2527981 using TICA FFIs in sector 27, but feel free to substitute a favorite target of your own. To find our target, we will filter with the database criteria: <span class="sb" title="Literal.String.Backtick">`coordinates`</span>, <span class="sb" title="Literal.String.Backtick">`target_name`</span>, <span class="sb" title="Literal.String.Backtick">`dataproduct_type`</span>, and <span class="sb" title="Literal.String.Backtick">`sequence_number`</span>.
The <span class="sb" title="Literal.String.Backtick">`coordinates`</span> criteria will filter for the coordinates of our target, while the <span class="sb" title="Literal.String.Backtick">`target_name`</span> will filter for TICA FFIs and exclude all SPOC FFIs. <span class="sb" title="Literal.String.Backtick">`dataproduct_type`</span> ensures we get image observations, and the <span class="sb" title="Literal.String.Backtick">`sequence_number`</span> will be used to filter for only sector 27 observations. With this information, let's compile our query...
</code></pre>
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<p>
You can download FFIs for any sector, camera, and CCD type. This notebook will generate cutouts for target TIC 2527981 using TICA FFIs in sector 27, but feel free to substitute a favorite target of your own. To find our target, we will filter with the database criteria:
<code>
coordinates
</code>
,
<code>
target_name
</code>
,
<code>
dataproduct_type
</code>
, and
<code>
sequence_number
</code>
.
</p>
<p>
The
<code>
coordinates
</code>
criteria will filter for the coordinates of our target, while the
<code>
target_name
</code>
will filter for TICA FFIs and exclude all SPOC FFIs.
<code>
dataproduct_type
</code>
ensures we get image observations, and the
<code>
sequence_number
</code>
will be used to filter for only sector 27 observations. With this information, let's compile our query...
</p>
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<textarea aria-labelledby="cell-11-source-label nb-source-label" class="nb-source" form="cell-11" id="cell-11-source" name="source" readonly rows="9"># We will pass in the coordinates as a Sky Coord object
coordinates = SkyCoord(289.0979, -29.3370, unit="deg")
obs = Observations.query_criteria(coordinates=coordinates,
target_name='TICA FFI',
dataproduct_type='image',
sequence_number=27)
obs</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># We will pass in the coordinates as a Sky Coord object</span>
<span class="n" title="Name">coordinates</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">SkyCoord</span><span class="p" title="Punctuation">(</span><span class="mf" title="Literal.Number.Float">289.0979</span><span class="p" title="Punctuation">,</span> <span class="o" title="Operator">-</span><span class="mf" title="Literal.Number.Float">29.3370</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">unit</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"deg"</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">obs</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">query_criteria</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">coordinates</span><span class="o" title="Operator">=</span><span class="n" title="Name">coordinates</span><span class="p" title="Punctuation">,</span>
<span class="n" title="Name">target_name</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'TICA FFI'</span><span class="p" title="Punctuation">,</span>
<span class="n" title="Name">dataproduct_type</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'image'</span><span class="p" title="Punctuation">,</span>
<span class="n" title="Name">sequence_number</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">27</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">obs</span>
</code></pre>
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{'scrolled': True, 'execution': {'iopub.status.busy': '2024-02-02T04:06:30.963104Z', 'iopub.execute_input': '2024-02-02T04:06:30.963282Z', 'iopub.status.idle': '2024-02-02T04:06:45.709374Z', 'shell.execute_reply': '2024-02-02T04:06:45.708764Z'}}
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9
</td>
<td class="nb-outputs" role="none">
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<span class="visually-hidden" id="cell-11-outputs-len">
has 1 output
</span>
<div>
<i>
Table masked=True length=1
</i>
<table class="table-striped table-bordered table-condensed" id="table139871813850544">
<thead>
<tr>
<th>
intentType
</th>
<th>
obs_collection
</th>
<th>
provenance_name
</th>
<th>
instrument_name
</th>
<th>
project
</th>
<th>
filters
</th>
<th>
wavelength_region
</th>
<th>
target_name
</th>
<th>
target_classification
</th>
<th>
obs_id
</th>
<th>
s_ra
</th>
<th>
s_dec
</th>
<th>
dataproduct_type
</th>
<th>
proposal_pi
</th>
<th>
calib_level
</th>
<th>
t_min
</th>
<th>
t_max
</th>
<th>
t_exptime
</th>
<th>
em_min
</th>
<th>
em_max
</th>
<th>
obs_title
</th>
<th>
t_obs_release
</th>
<th>
proposal_id
</th>
<th>
proposal_type
</th>
<th>
sequence_number
</th>
<th>
s_region
</th>
<th>
jpegURL
</th>
<th>
dataURL
</th>
<th>
dataRights
</th>
<th>
mtFlag
</th>
<th>
srcDen
</th>
<th>
obsid
</th>
<th>
objID
</th>
<th>
objID1
</th>
<th>
distance
</th>
</tr>
</thead>
<thead>
<tr>
<th>
str7
</th>
<th>
str4
</th>
<th>
str4
</th>
<th>
str10
</th>
<th>
str4
</th>
<th>
str4
</th>
<th>
str7
</th>
<th>
str8
</th>
<th>
str1
</th>
<th>
str25
</th>
<th>
float64
</th>
<th>
float64
</th>
<th>
str5
</th>
<th>
str17
</th>
<th>
int64
</th>
<th>
float64
</th>
<th>
float64
</th>
<th>
float64
</th>
<th>
float64
</th>
<th>
float64
</th>
<th>
str1
</th>
<th>
float64
</th>
<th>
str3
</th>
<th>
str1
</th>
<th>
int64
</th>
<th>
str117
</th>
<th>
str1
</th>
<th>
str1
</th>
<th>
str6
</th>
<th>
bool
</th>
<th>
float64
</th>
<th>
str8
</th>
<th>
str9
</th>
<th>
str9
</th>
<th>
float64
</th>
</tr>
</thead>
<tbody>
<tr>
<td>
science
</td>
<td>
HLSP
</td>
<td>
TICA
</td>
<td>
Photometer
</td>
<td>
TESS
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
TICA FFI
</td>
<td>
--
</td>
<td>
hlsp_tica_s0027-cam1-ccd4
</td>
<td>
292.5180823224232
</td>
<td>
-33.62274382166828
</td>
<td>
image
</td>
<td>
Michael Fausnaugh
</td>
<td>
3
</td>
<td>
59035.77807636512
</td>
<td>
59060.13917620387
</td>
<td>
475.2
</td>
<td>
600.0
</td>
<td>
1000.0
</td>
<td>
--
</td>
<td>
59246.0
</td>
<td>
N/A
</td>
<td>
--
</td>
<td>
27
</td>
<td>
POLYGON 298.489297 -26.919235 301.396094 -38.579035 285.694884 -40.219585 285.275975 -28.732615 298.489297 -26.919235
</td>
<td>
--
</td>
<td>
--
</td>
<td>
PUBLIC
</td>
<td>
False
</td>
<td>
nan
</td>
<td>
96814766
</td>
<td>
185636258
</td>
<td>
185636258
</td>
<td>
0.0
</td>
</tr>
</tbody>
</table>
</div>
</fieldset>
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<textarea aria-labelledby="cell-12-source-label nb-source-label" class="nb-source" form="cell-12" id="cell-12-source" name="source" readonly rows="3">This is the sector 27 FFI that contains TIC2527981. Note that the `s_ra` and `s_dec` listed above are the center of the CCD that contains the target, not the coordinates of the target itself.
To demonstrate this, let's plot the `s_region` metadata, the `s_ra` and `s_dec` coordinates, and the input coordinates of our target to see where it lands relative to sector 27, camera 1, CCD 4.</textarea>
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<pre><span></span><code>This is the sector 27 FFI that contains TIC2527981. Note that the <span class="sb" title="Literal.String.Backtick">`s_ra`</span> and <span class="sb" title="Literal.String.Backtick">`s_dec`</span> listed above are the center of the CCD that contains the target, not the coordinates of the target itself.
To demonstrate this, let's plot the <span class="sb" title="Literal.String.Backtick">`s_region`</span> metadata, the <span class="sb" title="Literal.String.Backtick">`s_ra`</span> and <span class="sb" title="Literal.String.Backtick">`s_dec`</span> coordinates, and the input coordinates of our target to see where it lands relative to sector 27, camera 1, CCD 4.
</code></pre>
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<p>
This is the sector 27 FFI that contains TIC2527981. Note that the
<code>
s_ra
</code>
and
<code>
s_dec
</code>
listed above are the center of the CCD that contains the target, not the coordinates of the target itself.
</p>
<p>
To demonstrate this, let's plot the
<code>
s_region
</code>
metadata, the
<code>
s_ra
</code>
and
<code>
s_dec
</code>
coordinates, and the input coordinates of our target to see where it lands relative to sector 27, camera 1, CCD 4.
</p>
</td>
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2024-02-02T04:06:45.717700+00:00
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<textarea aria-labelledby="cell-13-source-label nb-source-label" class="nb-source" form="cell-13" id="cell-13-source" name="source" readonly rows="29"># Extract the polygon vertices, then store them in separate lists
polygon = obs['s_region'][0]
split = polygon.split(' ')
# Removing the "POLYGON" string
split.pop(0)
# Storing the RAs and Decs separately
ras, decs = split[::2], split[1::2]
# Now we convert our RAs/Decs into radians and wrap them around 180 degrees
coords = []
for ra, dec in zip(ras, decs):
ra_rad, dec_rad = convert_coords(ra, dec)
coords.append((ra_rad, dec_rad))
# We use Matplotlib's Path and PathPatch to plot the footprint of sector 27, camera 1, CCD 4
ax = plot_footprint(coords)
# Now let's plot our input target's RA, Dec on top of the footprint to see its relative position
target_ra, target_dec = coordinates.ra.degree, coordinates.dec.degree
target_coords = convert_coords(target_ra, target_dec)
ax.scatter(target_coords[0], target_coords[1], color='k', label='target coords')
# And lastly, let's plot the s_ra and s_dec coordinates to see what they represent
s_coords = convert_coords(obs['s_ra'][0], obs['s_dec'][0])
ax.scatter(s_coords[0], s_coords[1], color='grey', label='s_ra, s_dec')
ax.set_title('Sector 27, Camera 1, CCD 4 Footprint' + '\n' + ' ')
plt.legend()
plt.show()</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="c1" title="Comment.Single"># Extract the polygon vertices, then store them in separate lists</span>
<span class="n" title="Name">polygon</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">obs</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'s_region'</span><span class="p" title="Punctuation">][</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">split</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">polygon</span><span class="o" title="Operator">.</span><span class="n" title="Name">split</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">' '</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Removing the "POLYGON" string</span>
<span class="n" title="Name">split</span><span class="o" title="Operator">.</span><span class="n" title="Name">pop</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Storing the RAs and Decs separately</span>
<span class="n" title="Name">ras</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">decs</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">split</span><span class="p" title="Punctuation">[::</span><span class="mi" title="Literal.Number.Integer">2</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">split</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">::</span><span class="mi" title="Literal.Number.Integer">2</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># Now we convert our RAs/Decs into radians and wrap them around 180 degrees</span>
<span class="n" title="Name">coords</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[]</span>
<span class="k" title="Keyword">for</span> <span class="n" title="Name">ra</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">dec</span> <span class="ow" title="Operator.Word">in</span> <span class="nb" title="Name.Builtin">zip</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">ras</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">decs</span><span class="p" title="Punctuation">):</span>
<span class="n" title="Name">ra_rad</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">dec_rad</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">convert_coords</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">ra</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">dec</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">coords</span><span class="o" title="Operator">.</span><span class="n" title="Name">append</span><span class="p" title="Punctuation">((</span><span class="n" title="Name">ra_rad</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">dec_rad</span><span class="p" title="Punctuation">))</span>
<span class="c1" title="Comment.Single"># We use Matplotlib's Path and PathPatch to plot the footprint of sector 27, camera 1, CCD 4</span>
<span class="n" title="Name">ax</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">plot_footprint</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">coords</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Now let's plot our input target's RA, Dec on top of the footprint to see its relative position</span>
<span class="n" title="Name">target_ra</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">target_dec</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">coordinates</span><span class="o" title="Operator">.</span><span class="n" title="Name">ra</span><span class="o" title="Operator">.</span><span class="n" title="Name">degree</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">coordinates</span><span class="o" title="Operator">.</span><span class="n" title="Name">dec</span><span class="o" title="Operator">.</span><span class="n" title="Name">degree</span>
<span class="n" title="Name">target_coords</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">convert_coords</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">target_ra</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">target_dec</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">scatter</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">target_coords</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">target_coords</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">color</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'k'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">label</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'target coords'</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># And lastly, let's plot the s_ra and s_dec coordinates to see what they represent</span>
<span class="n" title="Name">s_coords</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">convert_coords</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">obs</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'s_ra'</span><span class="p" title="Punctuation">][</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">obs</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'s_dec'</span><span class="p" title="Punctuation">][</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">])</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">scatter</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">s_coords</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">s_coords</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">color</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'grey'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">label</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'s_ra, s_dec'</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_title</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'Sector 27, Camera 1, CCD 4 Footprint'</span> <span class="o" title="Operator">+</span> <span class="s1" title="Literal.String.Single">'</span><span class="se" title="Literal.String.Escape">\n</span><span class="s1" title="Literal.String.Single">'</span> <span class="o" title="Operator">+</span> <span class="s1" title="Literal.String.Single">' '</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">legend</span><span class="p" title="Punctuation">()</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">show</span><span class="p" title="Punctuation">()</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:06:45.717271Z', 'iopub.execute_input': '2024-02-02T04:06:45.717700Z', 'shell.execute_reply': '2024-02-02T04:06:46.189236Z', 'iopub.status.idle': '2024-02-02T04:06:46.189836Z'}}
</code></pre>
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29
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7niiiuYM2cOb7/9NkFBQdjZ2fH+++/zz3/+87T7ORrc3d0JDg4mLy/vrLYf+NtkZGRc8OfX1tbS1dVFbGzsKbfp6uripZde4t5776W7u1uu7e7p6UGSJCorK3F2dj5j4G3JzuXcPZtjWRAEQRBBsyAIwpgyTqXT0NAAQExMDJIkERUVRXx8/ClfZ2xCmZeXJ0/hNJxT1UBGRESwdetWlErloNrmwsJC+fkLsX//fq666ioyMzP5/PPPzzha8aeffookSRfUNBtg2bJl/OlPf+Ljjz8+Y9Dc0dHBzz//zPPPP89zzz0nry8pKbmgNAwnJiaGnJwcLr744vOuFf7yyy9xdHRk8+bNODg4yOvff//9kUrmOVu6dCnvvvsue/fuZcaMGafd9tJLL8XW1paPP/54RAYDMw5ut3jx4lNu09HRQU9PD6+++iqvvvrqkOejoqK48sorz3n6qYGM50pRUZHc3N6oqKhIfn7gdicrLCzE19dXrmU+0zEy3DFaXFyMs7PzqA/KZYpWDYIgCOZG9GkWBEEYBdu2bRu2tsbYj9HYZPPqq6/G1taW559/fsj2kiTJTX4nT55MVFQUb7zxBp2dnUO2MzJmwk/e5rLLLkOv1/O3v/1t0Pr169ejUCguqIl0QUEBl19+OZGRkXz77bdnVdP1z3/+k/DwcHlKnPM1Y8YMlixZwj/+8Y9hAyGNRsOqVauA/6tVO/l7fuONNy4oDcO5/vrrqaur43//93+HPKdSqejt7T3je9ja2qJQKAb1Wa2srLyggO9CPf7447i4uHDHHXfQ1NQ05PmysjJ5CrSwsDDuvPNOfvrpJ956660h2xoMBl577TVqa2vP+Lm//PILL774IlFRUactaPH39+c///nPkGX+/Pk4Ojryn//8h6eeeuoc9niozMxM/P39eeeddwY1mf/hhx/kcwEgKCiIjIwMNm7cOOh8zMvL46effuKyyy6T153qvDXau3fvoH7sNTU1fP3111xyySWnrC0eKWdKmyAIwnggapoFQRBGwQMPPEBfXx9XXXUViYmJaDQa9uzZw7/+9S8iIyNZsWIFcKJG8qWXXuKpp56isrKS3/zmN7i5uVFRUcF//vMf7rrrLlatWoWNjQ0bNmxg2bJlZGRksGLFCoKCgigsLOT48eNs3rwZQB4s6MEHH2Tx4sXY2tpyww03sGzZMubPn88zzzxDZWUlEydO5KeffuLrr7/m4YcfHjQY0LlQKpUsXryYjo4OVq9eLQ+CZBQTEzOkRjIvL4/c3FyefPLJU9Zibd++nfnz57NmzRrWrl172jR8+OGHXHLJJVx99dUsW7aMiy++GBcXF0pKSvjss89oaGhg3bp1uLu7M2fOHF599VW0Wi0hISH89NNPVFRUnNe+n87NN9/M559/zj333MO2bduYNWsWer2ewsJCPv/8czZv3iy3OjiVyy+/nNdff50lS5Zw44030tzczN///ndiY2PJzc09Yxq6urrkYHX37t0A/O1vf8PT0xNPT0/uv/9+edtbb72VjRs3UlFRcdrpxWJiYvjnP/8pT8V1yy23kJqaKh/fX3zxBbfeequ8/WuvvUZZWRkPPvggmzZtYunSpXh5eVFdXc0XX3xBYWEhN9xww6DP+OGHHygsLESn09HU1MQvv/zCli1biIiI4L///e+gwbdO5uzszG9+85sh67/66isOHDgw7HPnys7Ojj//+c+sWLGCuXPn8rvf/U6ecioyMpJHHnlE3vYvf/kLl156KTNmzOD222+Xp5zy8PAYdFwbz9tnnnmGG264ATs7O5YtWyYHrKmpqSxevHjQlFPABffbPhtnSpsgCMK4YJIxuwVBEKzcDz/8IN12221SYmKi5OrqKtnb20uxsbHSAw88IDU1NQ3Z/ssvv5SysrIkFxcXycXFRUpMTJTuu+8+qaioaNB2u3btkhYtWiS5ublJLi4uUnp6+qDpYXQ6nfTAAw9Ifn5+kkKhGDTNjFKplB555BEpODhYsrOzk+Li4qS//OUvg6askqQTU9Hcd999Z7WfFRUVw07PY1wGTsNk9OSTT0qAlJube8r3/eabbyRAeuedd84qHX19fdK6deukqVOnyt93XFyc9MADDwyaUqe2tla66qqrJE9PT8nDw0O67rrrpPr6egmQ1qxZI29nnKKnpaVl0OecauqiuXPnSikpKYPWaTQa6c9//rOUkpIiOTg4SF5eXtKUKVOk559/Xurq6pK3O933/d5770lxcXGSg4ODlJiYKL3//vvDTh80nNP9Nsbpk4yuueYaycnJ6ayn7iouLpbuvPNOKTIyUrK3t5fc3NykWbNmSW+99ZbU398/aFudTif94x//kGbPni15eHhIdnZ2UkREhLRixYpB01EZp5wyLvb29lJgYKC0aNEi6c0335S6u7vPKm3DOZ8pp07+7U/2r3/9S5o0aZLk4OAgeXt7S7///e+l2traIdtt3bpVmjVrluTk5CS5u7tLy5Ytk/Lz84ds9+KLL0ohISGSjY3NoCmejMfHxx9/LB8LkyZNkrZt23bW6T7VlFPDHXcRERFDzttTpU0QBGG8UEiSGO1BEARBMC+PP/44n376KaWlpYP68wqjIyAggFtuuYW//OUvpk6KcBKFQsF99903pGuFIAiCMHZEn2ZBEATB7Gzbto1nn31WBMxj4Pjx46hUKp544glTJ0UQBEEQzJLo0ywIgiCYnYMHD5o6CeNGSkqKPDWTIAiCIAhDiZpmQRAEQRAEQRAEQTgFUdMsCIIgCIJgpsTQM4IgCKYnapoFQRAEQRAEQRAE4RRE0CwIgiAIgiAIgiAIpyCCZkEQBEEQBEEQBEE4BRE0C4IgCIIgCIIgCMIpiKBZEARhDERGRqJQKIYs9913HwDz5s0b8tw999wz6D3++9//Eh8fT0JCAt9++60pdkMQBEEQBGHcUUhiWEZBEIRR19LSgl6vl//Py8tj0aJFbNu2jXnz5jFv3jzi4+N54YUX5G2cnZ1xd3cHQK1WExsby/vvv48kSdx2222UlZVhb28/5vsiCIIgCIIwnogppwRBEMaAn5/foP9feeUVYmJimDt3rrzO2dmZwMDAYV+vVquxtbUlIyMDgAkTJqBWq0XQLAiCIAiCMMpE82xBEIQxptFo+Pjjj7nttttQKBTy+k8++QRfX19SU1N56qmn6Ovrk59zd3dnxYoVBAUFERwczMqVK3FzczNF8gVBEARBEMYV0TxbEARhjH3++efceOONVFdXExwcDMC7775LREQEwcHB5Obm8sQTT3DRRRexadOmQa/t6urCxsZGBMyCIAiCIAhjRATNgiAIY2zx4sXY29vzzTffnHKbX375hYsvvpjS0lJiYmLGMHWCIAiCIAjCQKJ5tiAIwhiqqqpi69at3HHHHafdbtq0aQCUlpaORbIEQRAEQRCEUxBBsyAIwhh6//338ff35/LLLz/tdkePHgUgKChoDFIlCIIgCIIgnIoYPVsQBGGMGAwG3n//fZYvX86ECf93+S0rK+Of//wnl112GT4+PuTm5vLII48wZ84c0tPTTZhiQRAEQRAEQQTNgiAIY2Tr1q1UV1dz2223DVpvb2/P1q1beeONN+jt7SUsLIxrrrmGP/zhDyZKqSAIgiAIgmAkBgITBEEQBEEQBEEQhFMQfZoFQRAEQRAEQRAE4RRE0CwIgiAIgiAIgiAIpyCCZkEQBEEQBEEQBEE4BRE0C4IgCIIgCIIgCMIpiKBZEARBEARBEARBEE5BBM2CIAiCIAiCIAiCcAoiaBYEQRAEQRAEQRCEU5hg6gQIgiAIgnCCwWBAq9Wi1WqRJGnIYjAYhqwzUigUZ7XY2tpib2+Pra0tCoXChHsrCIIgCJZBBM2CIAiCMIAkSfT19aFUKuWlp6dnyOPe3l40Gg0ajQa1Wn3Wj0/3vE6nG7P9VCgUODg4YG9vj729/Xk/HrjO0dERV1dX3NzccHNzG/R44P92dnZjtp+CIAiCcKEU0sBiakEQBEGwUJIkoVQqaWtrk5f29vYzBr8n/9/T0yPX4Nrb2w8b9Lm5ueHs7IyDg8M5BZVn+3jChAlDaohtbGxOWXs8XK30qRa9Xo9Wq72g4P5Uj1UqFT09PcN+zwO/VwcHh7MKrgc+9vDwwMfHZ9Aigm9BEARhLIigWRAEQTA7Op2O9vb2QQHwyUtra+uQAFmr1WJra4u3tzc+Pj54e3vj7u5+VkHZyf+7urpib29v6q/CahgMBvr6+k5ZWHG6ggylUklXV5f8u/f29gLg7u4+JJAeuPj6+g5Z5+LiIpqlC4IgCOdEBM2CIAjCmOjv76ehoYH6+nr5r/FxS0vLoAC4s7MTACcnp9MGQMM95+7ujo2NGOfSmqnV6rMqSDm5UEWSJOzt7YccO4GBgQQFBREcHCz/DQ4OxsfHRwTYgiAIggiaBUEQhAszMBgeGBCfvK6jowNbW1sCAwMHBSdBQUH4+fkNGxQ7OTmZevcEK6HX6+ns7Bw20G5sbBxy3HZ3d2NnZ0dQUNCgQFoE14IgCOOPCJoFQRCEU+ru7qaiooLKykqqqqrOKRgeLsDw9fXF1tbW1LslCGfU29s7JJAerkBouODa+Dc0NJTIyEiioqIICQkRx74gCIKFEkGzIFiQuro6nnjiCX744Qf6+vqIjY3l/fffJzMzEzgxENKaNWv43//9Xzo7O5k1axYbNmwgLi5Ofo+9e/eycuVKOjs7efbZZ7n99ttNtTuCGVAqlVRWVlJZWSkHxwOXjo4O3N3diYqKIjw8nJCQkGEDYxEMC+OVMbgeLqCura2loqKC2tpabGxsCAsLIzIyUl6ioqLkx8HBweIcEoSzZBzQUBDOxM7ObkSurSJoFgQL0dHRwaRJk5g/fz4rV67Ez8+PkpISYmJiiImJAeDPf/4zf/rTn9i4cSNRUVE8++yzHDt2jPz8fBwdHQFITk7mxRdfJCgoiFtuuYVt27YRFhZmyl0TRlFPT8+QQHhgcNze3o6rq6uceR+YiTcunp6eoumpIFwArVZLbW3tKc/D2tpabG1tCQ8PH/YcNAbVoq++MN5JkkRjY6M87oUgnA1PT08CAwMvKC8jgmZBsBBPPvkku3fvZufOncM+L0kSwcHBPPbYY6xatQqArq4uAgIC+OCDD7jhhhsAiIiIYPv27fj7+zNv3jw2btxIcnLymO2HMPI6OjooKiqSl5KSEjkz3traipOTE9HR0YMy4wMfe3l5iaBYEExIo9HIQfXJLT5KS0tpampiwoQJREREEBkZSXR0NAkJCfISGRnJhAkTTL0bgjDqGhoa6OzsxN/fH2dnZ3HvEk5LkiT6+vpobm7G09OToKCg834vETQLgoVITk5m8eLF1NbWsmPHDkJCQrj33nu58847ASgvLycmJobs7GwyMjLk182dO5eMjAzefPNNAN577z3uu+8+dDodK1eu5K233jLF7gjnSKvVUlZWRnFx8aAAuaioiJaWFvz9/eUMdHx8/KCa45qaGvr6+pgxY4bIYAiCBWlra2Pv3r1kZWXR2to6KJA2nv+lpaUAxMTEDAqkjYuvr6+J90IQRoZer6e4uBh/f398fHxMnRzBgrS1tdHc3Ex8fPx5N9UWxZKCYCHKy8vZsGEDjz76KE8//TQHDx7kwQcfxN7enuXLl9PY2AhAQEDAoNcFBATIzwHcfvvt3HDDDWg0Gry8vMZ0H4TTkySJ5ubmIUFxUVER5eXl2NnZERcXJwfGc+bMkTPGnp6ep3xfd3d3tm3bRllZGbGxsWO3Q4IgnDeNRsPhw4dJSkrC09MTT0/PYc9fnU5HVVXVoOvFRx99RFFREQ0NDXh7ew8JpOPj44mNjcXBwcEEeyYI58fYh9nZ2dnEKREsjfGY0Wq1ImgWBGtnMBjIzMzkj3/8IwCTJk0iLy+Pd955h+XLl5/Te7m4uODi4jIayRTOgl6vp6ysjGPHjlFQUEBRUZFcg9zV1UVoaKicuV20aBH3338/CQkJhIeHn1efRjs7OzIzM9mzZw/u7u74+/uPwl4JgjBSDAYDhw4dwsPDg+jo6NNuO2HCBHlsi8suu2zQc93d3YNap2RnZ/PZZ59RXFyMWq0mMjJyUDCdkpJCWlraaQvhBMHURIsp4VyNxDEjgmZBsBBBQUFD+h4nJSXx5ZdfAhAYGAhAU1PToD4bTU1Ng5prC2Orra2N3NxccnNzOXbsGLm5ueTl5aHX60lOTiYpKYmEhAQuv/xyuQZoNAo0vL29mThxIgcPHmTmzJmilYEgmClJksjOzkaj0ZCVlXVBmT13d3cyMzPlGRaMDAYDdXV1g2qnN23axIsvvkh9fT3h4eGkp6fLS1paGvHx8aLftCAI45a4+gmChZg1axZFRUWD1hUXFxMREQFAVFQUgYGB/Pzzz3KQ3N3dzf79+1m5cuVYJ3fc0Wg0FBYWyoGxcTk5A/rYY4+Rnp5OXFzcmGdAw8LC0Gg07NmzhxkzZuDt7T2mny8IwukZDAays7PlKQNH6xphnP4qLCyMhQsXDnqutbWVY8eOydey1157jby8PAwGA8nJyYOC6fT0dNFyRRCEcUEMBCYIFsJYQ/j8889z/fXXc+DAAe68807effddfv/73wMnppx65ZVXBk05lZubO2jKKeHCSJJEfX39kOC4oKAAR0dH0tLS5JoZ419za+pYXl5Ofn4+U6ZMuaCRJAVBGDlarZZDhw7R39/PjBkzzOqabexScnKrmfLycvz9/YcE0klJSWaVfsE69Pf3U1FRQVRUlDi+xqF58+aRkZHBG2+8cc6vHYljR9Q0C4KFmDp1Kv/5z3946qmneOGFF4iKiuKNN96QA2aAxx9/nN7eXu666y46OzvJysrixx9/FDeX82QwGCgqKuLgwYMcPnxYzjB2dHQQFxcnB8bXXnst6enpREZGWsQ8qtHR0Tg6OnL48GHi4+OJi4sTfcQEwYSUSiUHDx7EycmJrKws7OzsTJ2kQWxtbYmPjyc+Pp5rr71WXq9UKsnLy5Ovjf/4xz/Izc2lt7eX+Ph40tPTycjIkJuIm1sBojA+6fV6du7cSUNDA0FBQcyePfu8B4c6GxcS7I0Wc0yTuRNBsyBYkKVLl7J06dJTPq9QKHjhhRd44YUXxjBV1kGSJKqqqjh48KC8HD58GK1Wy6RJk8jMzOSmm24iPT2d5ORkix9ILTg4GGdnZw4ePEhbWxupqam4ubmZOlmCMK7odDpqa2s5fvw4kZGRJCUlWUTBm5GbmxszZsxgxowZ8jpJkqipqZED6cOHD/POO+9QVVVFXFwcU6dOlZdJkyaJkZCFMbVp0yYeeughamtr5XWhoaG8+eabXH311SZM2ZlpNBrs7e1NnYxRJUkSer3eLMdPsJwrsyAIwghqamri22+/Zc2aNVx22WX4+/sTGxvLH//4Rzo7O7nxxhv59ddf6e7uZs+ePfz1r3/l9ttvZ+rUqRYfMBsMBjo6OmhtbcXV1ZXW1la2bdtGfX29qZMmCOOGWq1m79695OTkYG9vj8FgoLGxkf7+flMn7YIoFArCw8NZunQpTz/9NF988QWVlZU0NTXxxhtvEB8fz9atW7n66qtxd3dn4sSJclej7OxseVohQRhpmzZt4tprrx0UMAPU1dVx7bXXsmnTphH/zFtvvZUdO3bw5ptvolAoUCgUVFZWotfruf3224mKisLJyYmEhATefPPNIa/9zW9+w8svv0xwcDAJCQkA7Nmzh4yMDBwdHcnMzOSrr75CoVBw9OhR+bV5eXlceumluLq6EhAQwM0330xra+tp0zQctVrNE088QVhYGA4ODsTGxvLee+/Jz+/YsYOLLroIBwcHgoKCePLJJ9HpdINe/+CDD+Lv74+joyNZWVkcPHhQfn779u0oFAp++OEHpkyZgoODA7t27aK3t5dbbrkFV1dXgoKCeO2114ak7e233yYuLg5HR0cCAgIGtYIZDeYXxguCIIywrq4uDh06NKgWuaamhoSEBKZOncqSJUt49tlnycjIwMnJydTJHXFarZaOjg7a2tpob2+no6MDGxsbfHx88PX1JSEhgd7eXnJycqipqSEtLU3U/gjCKJEkierqavLz8/Hx8WHu3Ln09PTQ3t5OUVER3d3duLi44O3tjY+PD97e3ri6ulp8Fwp/f38uu+wyeVosY4208Zr82WefsXr1ajQaDRkZGYNqpOPj4y2qBl4wP3q9noceeojhhnKSJAmFQsHDDz/MlVdeOaJNtd98802Ki4tJTU2VWwH6+flhMBgIDQ3liy++wMfHhz179nDXXXcRFBTE9ddfL7/+559/xt3dnS1btgAnBnhdtmwZl112Gf/85z+pqqri4YcfHvSZnZ2dLFiwgDvuuIP169ejUql44oknuP766/nll19Omabh3HLLLezdu5e//vWvTJw4kYqKCjn4rqur47LLLuPWW2/lww8/pLCwkDvvvBNHR0fWrl0LnOg2+OWXX7Jx40YiIiJ49dVXWbx4MaWlpYMGI33yySdZt24d0dHReHl5sXr1anbs2MHXX3+Nv78/Tz/9NEeOHJEHuj106BAPPvggH330ETNnzqS9vZ2dO3de8O91OmIgMEEQrIparebIkSMcOHBAzowVFxcTHh4+KBM2ZcoUPDw8TJ3cUdHf3y8HyG1tbXR1deHs7DwoE+7m5jYkE65Wq8nPz6euro64uDhiYmLMsomUIFiq9vZ28vLy6O/vJy0tjcDAwCHnoVarlc9dYyHXhAkTBp2/np6eVhlEGgwGiouLBxVyZmdnY29vz5QpU+Tr97Rp0wgLCzN1coUxdiGDOW3fvp358+efcbtt27Yxb96880zh8M62//D9999PY2Mj//73v4ETNcI//vgj1dXVcrPsd955hz/84Q/U1tbK38E//vEP7rzzTrKzs8nIyOCll15i586dbN68WX7v2tpawsLCKCoqIj4+/qzSVFxcTEJCAlu2bBkyyj7AM888w5dffklBQYF8HXv77bd54okn6OrqQqVS4eXlxQcffMCNN94InLi+RUZG8vDDD7N69Wr5d/nqq6+48sorAejp6cHHx4ePP/6Y6667Djhx7QwNDeWuu+7ijTfeYNOmTaxYsYLa2tqz6lomBgITBGHc6+zsZM+ePezatYudO3dy8OBB3NzcuOiii5g6dSq/+93vmDp1qlVPi6JSqWhtbZWXvr4+3N3d8fHxITY2Fh8fn7OqQXdwcGDSpElERESQl5dHRUUFiYmJhIeHW2UGXRDGilKppLCwkKamJmJjY4mNjT1lgZSdnR0BAQEEBAQAJ2rIurq6aGtro62tjeLiYgwGA15eXvj6+uLn52c1QbSNjQ2JiYkkJiZy0003AScy2cePH5eD6Jdffpljx44REhLC7NmzycrKYvbs2RbXH1wYWw0NDSO63Uj4+9//zv/7f/+P6upqVCqV3MpioLS0tEH9mIuKikhPTx8U+F100UWDXpOTk8O2bdtwdXUd8pllZWXEx8efVfqOHj2Kra0tc+fOHfb5goICZsyYMajgb9asWfT09FBbW0tnZydarZZZs2bJz9vZ2XHRRRdRUFAw6L0GziVfVlaGRqNh2rRp8jpvb2+5eTrAokWLiIiIIDo6miVLlrBkyRKuuuqqUW0lJ4JmQRAsSl1dHTt37pSD5GPHjhETE0NWVhYrVqzgvffes/rRoNVq9aAguaenBw8PD/z8/EhPT8fb2/uCRt/19vZm9uzZ1NfXU1hYSElJCfHx8YSFhYlMqSCcA6VSSXFxMfX19YSFhXHxxRefcxcQW1tbvL295aaMkiShVCppa2ujtbWV8vJy9Hq93N3C19cXT09Pq7kG2tnZkZGRQUZGBnfeeSdw4nvdv38/O3fu5IsvvuCxxx7D0dGRWbNmyUH0lClTrH7QJOHsne30imM1DeNnn33GqlWreO2115gxYwZubm785S9/Yf/+/YO2O58xVHp6eli2bBl//vOfhzx3Lvs3lt3VznU/3dzcOHLkCNu3b+enn37iueeeY+3atRw8eHDURukXQbMgCGZLkiQKCwsHBcnV1dVkZGSQlZXFs88+y6xZs6x+rmGtVjsoSO7u7sbd3R1fX1+Sk5Px8fEZ8cyhQqEgJCSE4OBgamtrKSoqorCwkJiYGCIiIsxuShxBMCft7e2UlpbS1NREaGgoCxYsGLEBBBUKBe7u7ri7uxMVFYUkSXR3d8vXh+LiYgA5gPb19cXd3d1qgmg4kWFeuHCh3GRUq9WSnZ0t3yv+8pe/0NPTw7Rp0+QgesaMGbi7u5s45YKpzJ49m9DQUOrq6obt16xQKAgNDWX27Nkj/tn29vbo9fpB63bv3s3MmTO599575XVlZWVnfK+EhAQ+/vhj1Go1Dg4OAIMG1gKYPHkyX375JZGRkads0TJcmk6WlpaGwWBgx44dwzbPTkpK4ssvv5T7hBv3y83NjdDQUDlvsnv3biIiIoAT5+rBgweH9MMeKCYmBjs7O/bv3094eDgAHR0dFBcXD6r1njBhgnwdWLNmDZ6envzyyy+jNgq6CJoFQTAbGo1mUMbHOILi9OnTycrK4qabbmL69OlWn/HR6/W0tbXR0tJCa2srnZ2duLq64uvrS3x8PL6+vvLNcrQpFArCwsIIDQ2loaGB0tJSioqKCAsLIyoqSkxTJQj/P71eT319PRUVFSiVSiIjI1m4cOGo19YoFAo8PDzw8PAgJiYGSZLo7OyktbWV5uZmCgoKsLW1lQNoPz+/YZttWjJjk8+LLrqIxx57DEmSKCoqku8lK1eupKqqiokTJ8pBdFZWltUXuAr/x9bWljfffJNrr70WhUIxKHA2BnxvvPHGqMzXHBkZyf79+6msrMTV1RVvb2/i4uL48MMP2bx5M1FRUXz00UccPHiQqKio077XjTfeyDPPPMNdd93Fk08+SXV1NevWrRu0H/fddx//+7//y+9+9zsef/xxvL29KS0t5bPPPuMf//gHtra2w6bp5JZkkZGRLF++nNtuu00eCKyqqorm5mauv/567r33Xt544w0eeOAB7r//foqKilizZg2PPvooNjY2uLi4sHLlSlavXo23tzfh4eG8+uqr9PX1cfvtt59yH11dXbn99ttZvXo1Pj4++Pv788wzzwxK37fffkt5eTlz5szBy8uL77//HoPBMKgJ90gTQbMgCCbT39/P7t272bFjB7t27WLfvn04OTmRlZVFVlYWTz75JJMnT7b6JnbG5pbNzc00NzfT1taGg4MDfn5+REdH4+vra/JRvRUKBcHBwQQFBdHR0UFFRQXbt2/H09MTLy8vkpKSRiWzIQjmrrm5mcrKStra2rCzsyM6OpqwsDCTtcZQKBR4eXnh5eVFXFwcBoOBzs5OWlpaqK+v59ixYzg5OeHv74+/vz++vr5W13JEoVDIfaONTbrr6urkwtiXX36Z3NxcoqOj5SB6wYIFZwxYBMt29dVX8+9//3vYeZrfeOONUauhXLVqFcuXLyc5ORmVSkVFRQV333032dnZ/Pa3v0WhUPC73/2Oe++9lx9++OG07+Xu7s4333zDypUrycjIIC0tjeeee44bb7xR7uccHBzM7t27eeKJJ7jkkktQq9VERESwZMkSOfAcLk2RkZFDPm/Dhg08/fTT3HvvvbS1tREeHs7TTz8NQEhICN9//z2rV69m4sSJeHt7c/vtt/OHP/xBfv0rr7yCwWDg5ptvRqlUkpmZyebNm/Hy8jrtfhpbiyxbtgw3Nzcee+wxurq65Oc9PT3ZtGkTa9eupb+/n7i4OD799FNSUlLO6jc5H2L0bEEQxozBYODo0aNs2bKFrVu3smvXLnx8fJg/f76ccUlMTBwX/WY1Gg3Nzc20tLTQ3NyMVquVa4H8/f3NeooZtVpNfX091dXVdHZ2Ymtri42NDSEhIYSFheHl5WW2aReEkaDRaKirq6O2tpaOjg4UCsWgc8Db29tszwGdTifXQjc3N9PX14e3t7d87bGm/tCn09XVJQ8i+euvv7Jv3z7Cw8NZuHAhixYtYv78+fj4+Jg6mcIAIzECMpxoFbJz504aGhoICgpi9uzZFl3o+8knn7BixQq6urpMXsBurkbi2BFBsyAIo6qiooKtW7eyZcsWfvnlF7RaLfPnz5f7oSQkJIyLDJrBYKCjo0POqHZ2duLu7i7X9nh7e5v1TVur1dLY2EhtbS0tLS14eXkREhJCSEgI9vb2tLe3U1NTQ319PXZ2dnJ/aA8Pj3Hx+wrWz3gO1NfX09zcjIeHB6GhoYSGhmJnZ0dbWxt1dXXU1dUxYcIEQkJCCA0NNfv+xH19ffJ1qaWlBRsbGzmA9vf3v6DgxJL09PTw66+/yver/Px8Jk2axKJFi1i4cCGzZs0aN9+FuRqpoNnSffjhh0RHRxMSEkJOTg73338/8+bN4+OPPzZ10syWCJoFQTA77e3t/PLLL3LGo7q6mhkzZsgZj6lTp46buX/7+/tpamqiqalJzoz6+/vLGVJzv+nr9Xqampqoq6ujsbERV1dXQkNDCQkJOeW0DsbX1NfX09jYiKOjI0FBQQQGBpp17ZsgDEetVtPY2EhjYyPNzc24uroSHBxMSEjIKfsFGwwGmpubqauro6GhAScnJ/m8Mfe+xKcr3Btv53BDQwO//PILW7ZsYcuWLbS3tzN79my5wDcjI2NctIoyJyJoPuHVV1/l7bffprGxkaCgIH7zm9/w8ssvj+p0S5ZOBM2CIJicsV/y1q1b2bp1K0eOHCE5OVlu4jZnzhyzzyiOFOMotsZMdldXF56envKcq5ZQ6ypJEq2trdTW1lJfX4+9vb2c4T/XAdiMAXRjYyNNTU0A8nfh5+dn9X3VBcsjSRJdXV00NTXR3NxMR0cHnp6eBAYGEhwcfM7XMp1OR1NTE7W1tTQ3N+Pu7i6fT5aQ6ddoNLS0tMiFf4AcQPv7+1tdX+hTMc7kYLzPbdu2DXt7ey6++GL5Xjdcf1BhZImgWThfImgWBGHMGQwGcnJy5H7JO3fuxNvbW65Jvvjii8fViKR6vV7OVDY2NqLVagdlKi3lxt7T00N1dTU1NTVIkiQ3LR2p/o0Gg4H29nY5GFEqlXh6euLn54efnx9eXl5m3TxdsF59fX3ySPUtLS3o9Xr8/PzkAp6ROoc1Gg0NDQ3U1tbS1taGv78/YWFhBAYGWsSxL0kSHR0dcqFgT08PPj4+BAYGEhAQMG4KR+H/ps0xtqjat28fERER8n1w/vz58rzawsgRQbNwvkTQLAjCmFAqlWzZsoVvvvmG77//HpVKNahfcmJiotnXoI4klUo1qNm1g4MDAQEBBAYG4uPjYxEZYDiR8aurq6O6upquri4CAwMJCwvD399/1JsdqlQqmpub5UBFp9Ph7e2Nl5cXDg4OREREWMz3KFgO43RMLS0t9Pb20tbWRl9f36ACnOGmXhlpfX191NTUUFNTg0ajschB9Hp7e+XCwra2NpydneUAeiy+Q3OiVCr59ddf5cLkgoICpk+fztKlS1m2bBkpKSkW87uaMxE0C+dLBM2CIIyayspKvvnmG7755ht27NhBVFQUy5YtY+nSpcycOXPcNMszUiqVNDQ00NDQQFdXF15eXnKg7ObmZjEZIoPBQEtLC9XV1TQ2NuLu7k54eLg8oJcpGJvElpWV0dDQgMFgAE5MKeHt7S0H046OjhbzPQvmQa/X09XVRUdHB+3t7bS3t6NWq5EkCVdXV2JiYggJCTHZ9cxYe1tdXU1dXR2Ojo6EhYURFhZmUaPgarXaQc24DQYDAQEBBAUF4e/vP27GsTCqr6/n+++/55tvvuGnn37C39+fZcuWccUVVzB37lwcHBxMnUSLJIJm4XyJoFkQhBGj1+vZv3+/HCgXFRUxZ84cli5dSmJiIgaDgczMTAICAkyd1DFhDOQaGhqor6+nr68Pf39/goKCCAgIsLhMT3d3t1yzpVAo5Iy5m5ubSdMlSRK1tbUUFRUBkJiYSHBwMCqVSg5y2tvb6e7uxsHBAS8vLzw9PfHw8MDDw0ME0oJMr9ejVCrp6uqis7OTjo4Ouru7sbOzw8vLSy6A8fT0RKPRUFxcTE1NDaGhoSQkJJh8EB29Xk9DQwM1NTW0tLTg6+tLWFgYwcHBFtXqwlgQYCxk7O/vx9/fn+DgYAICAsZNgatWqyU7O5vm5mb6+/v5+eef+fbbb+ns7OSSSy5h2bJlXHbZZfj7+5s6qRZDBM3C+RJBsyAIF6S7u5vNmzfz7bff8v333yNJEpdddhlLly5l8eLFeHh4yNvW1tZy9OhR4uPjiYuLs8pARZIk2tvb5cyeWq0mMDBQri2xtMyeTqejrq6Oqqoqurq6CAoKIjw8HD8/P5P/fpIk0dDQQGFhITqdjoSEBMLCwk7ZpFOn0w0Khrq6uujp6cHBwQF3d/dBi5ubm0UFGcK5kSSJ/v5+uru7By1KpZIJEybIBSrGAhZnZ+dTHu89PT0UFRVRX19PeHg48fHxZlHDq1KpqK2tpbq6GrVaTWhoKBEREYOuyZZAkiSUSiX19fU0NDSgVCrx8/MjODiYwMBAiyt8PFu9vb3s378fBwcHpk6dKrfikSSJnJwcvv32W7755hsOHz7M1KlT5WbcaWlpJr82mzMRNAvnSwTNgiCcs/Lycrk2+ddffyUuLk5udj1jxozTBhudnZ0cOHAANzc3Jk+ebBUZHoPBQGtrqxwoGwwGgoKCCAoKws/PzyKDr66uLiorK6mtrcXJyYnIyEhCQ0PNYrRqSZJoamqisLAQtVpNfHw8ERER59X/UafT0d3dTVdX16DgSafT4eLigpubG66urri5ueHi4oKrqyt2dnYoFAqRMTVzkiQhSRK9vb309PTQ29uLUqmUF+NvPLCwxMPD47QB8ul0d3dTWFhIc3MzkZGRxMXFmcX1zVhrW1lZSX19Pe7u7kRERBASEmKRTZ57enrka21nZyc+Pj7y9dYcCitGQk1NDbm5uYSHh5OSknLaa1tjY6PcjHvLli34+PjIAfS8efNEYHgSETQL50sEzYIgnJFer2fPnj1yyXZpaSnz5s1j6dKlLF26lOjo6HN6P61Wy9GjR2lra2Py5MkW2bTMGCgb51G1tbWVM24+Pj4WOYCNcVCvqqoqlEolISEhREREmNXAQq2treTn59Pb20t8fDyRkZEjXihhrIUcGGAZA6/+/n4UCgWOjo54eXnh4uKCi4sLzs7OODk54eTkZJGFJJZKkiQ0Gg0qlYq+vj56e3vp6+ujp6eHtrY2JEnCxsYGV1dXuRDEuLi4uIxK0NjZ2UlhYSGtra3ExMQQGxtrNi1MtFotNTU1VFVV0dfXJ9c+e3p6mjpp50WlUskBdFtbGx4eHoSGhhIcHGyRAbRWqyU3N5fm5mYmTZpEYGDgOb2+v7+f7du3y/fqtrY2Fi1aJN+rx0vXqNMRQbN5+uCDD3j44Yfp7Ow0dVJOSQTNgiAMS6/Xs3PnTr744gu+/PJL9Ho9l19+OUuXLuWSSy455/l2TyZJEtXV1Rw7doyIiAiSkpLMvtbD2PTaOP+wQqEgJCSEkJAQswosz4VxJOCqqipqa2txdXUlIiKC0NBQs8now4lavIKCAlpbW4mNjSUmJmbMj5eenh727duHvb09kZGRqNVqent75UBNpVIhSRIODg5yEO3o6Cj/NS4ODg5m9d2aK4PBgFqtRq1W09/fLy8qlWrQotfrsbe3x8nJSS7AcHFxQaVSUVpaSlxcHAkJCWN+fra3t5Ofn49SqSQhIYHIyEizKUwznveVlZXU1dXh6upKZGSkSQc0u1BqtZrGxkZ5Oi5vb29CQkIIDg42ixr/M2ltbSU7OxtnZ2cmT558wUG/JEnk5eXJrcIOHTrErFmzuP7667n66qvPOSC3FiMVNBsMBqqrq1Eqlbi5uREeHm4257clEkGzIAgW5eRA2WAwcM0113DdddcxZ86cUQlSenp6yM7Opr+/n4kTJ5pdrbMxc1lXV0ddXR0Gg4Hg4GBCQkLw8fGxyEAZTvzWtbW1VFRU0NPTQ2hoKJGRkWZX46RSqSgsLKS2tpaIiAgSEhJMkgFuaWnh4MGDhIeHk5ycPGzmyFhDfXJQp1Kp5IBPrVZjMBiwtbXFwcFBXuzt7YcsGo0Gd3d3OcieMGGCRR9vWq0WrVZLZ2cnEyZMQK/Xo9FoBi3GINn4GMDe3h4HBwe5AGJgYYSTkxPOzs6nvDZ1dXWxf/9+vL29ycjIGPOCFmNXguPHjyNJEklJSQQHB5vV72hsYVJZWUlPTw9hYWFERUVdcMGoKfX391NfX09dXR0dHR34+fkREhJCUFCQ2RUKaLVa8vPzqampISkpiejo6FE5Purr6/nyyy/54osv2LNnD1lZWeMygB6JwKegoIAff/yR7u5ueZ27uztLliwhKSlppJI6KjQajVl0szqZCJoFQTB7pgiUTyZJEpWVleTn5xMUFERqaqrJL+rd3d1yoKxWqwkKCiIkJAQ/Pz+LLk3u7e2lsrKSqqoqHB0diYqKMrtaZTiRkSwpKaG8vJyAgACSkpJwdXUd83RIkkRFRQX5+fmkpaURERFxwe+n1Wrp7+8fEiSeHDx2d3dja2uLXq+XXz9hwoRBi62trfzXuNjY2Mh/jYtCoZAfA3KfbGPmXJIkDAaD/Lzxtm7sF2wwGOS/Axe9Xi//1el06PV6+fHAxTgFmK2tLQaDATs7O1xdXYcUFAwsRDAGyhd6vvX393Pw4EH0ej3Tpk0zSbNdg8FATU0NBQUFODk5kZKSgq+v75in40w6OzupqKigtrYWLy8voqOjCQwMtOhrXl9fH/X19dTW1qJUKvH39yckJITAwECTt25qaGggNzcXNzc3Jk6ciIuLy5h87ngOoC808CkoKODzzz8/5fPXX3/9qATO//73v3n++ecpLS3F2dmZSZMm8fXXX5/xmLn11lvp7Oxk6tSp/P3vf8fBwYGKigo++ugj3nzzTYqKinBxcWHBggW88cYb51RxkZOTw8MPP8yhQ4dQKBTExcXxP//zP2RmZp7xtR988AHPPfccra2tLF68mKysLF588cVBQfPXX3/N888/T35+PsHBwSxfvpxnnnlGPm87Ozt54okn+Oqrr+jq6iI2NpZXXnmFpUuXnvU+nAsRNAvCOGQOgfJwVCoVOTk5tLe3k5SUdN6DO52vvr4+ampqqKuro6+vj4CAAEJCQggICLDofqqSJNHS0kJFRQXNzc0EBgYSFRVlljXlBoOBiooKiouLcXNzIyUlBS8vL5OlJTc3l8bGRi666CK8vb3H7LNzc3Pp7e1l+vTpwIkBy7Ra7ZBg9ORgdWAQe3KAawx64f+C4YHBcUdHB15eXsMG1gMDb4VCMShANwbpA4P3k4P7CRMmYGdnh62tLe3t7ezZs4cFCxaM2RRNer2e3Nxcmpqaxvy3HEin01FWVkZpaSk+Pj4kJyebZY2uRqOhqqqKyspKDAYDkZGRREREWHwf0J6eHurq6qitrUWlUhEYGEhoaCj+/v5jeq9RKpXk5eXR0dFBSkoK4eHhJrsWj7cA+kICH4PBwJtvvjmohvlk7u7uPPTQQyN6PDU0NBAeHs6rr77KVVddhVKpZOfOndxyyy1nLEy+9dZb+fLLL7nqqqt44oknAEhJSeH//b//R1BQEAkJCTQ3N/Poo4/i6enJ999/f9bpSk1NZdKkSTzzzDPY2trKs6NMnDjxtK/bv38/M2fO5E9/+hO/+c1v+PHHH1mzZo3csg9g586dLF26lL/+9a/Mnj2bsrIy7rrrLm699VbWrFmDwWBg1qxZKJVK1q9fT0xMDPn5+dja2nLppZee9T6cCxE0C8I4Ya6B8nAaGxvJy8vD1taWtLS0Ua2R0Wq18rymbW1t+Pv7Exoaaha1EBdKq9VSXV1NRUUFOp2OiIgIIiMjzXKAHEmSaGxs5Pjx49jY2JCcnExAQIDJMpIajYaDBw+i1WrHvHayq6uLX3/9lXnz5o3ZHNharZbvv/+eyy67bExaHRw5cgS9Xs/UqVNH/bOMJEmivLycgoICMjIyCA0NHbPPPplaraaoqIiqqirCw8NJTEw0y363xubl5eXltLW1ERwcTFRUlMWO4WBknMaqtraW2tpaDAYDISEhhIeH4+7uPmr7ptVqKS4upry8nPDwcJKSkkzeqmqg8RBAX0jgU1lZycaNG8+43fLly4mMjDzPFA515MgRpkyZQmVl5Tm3drr11lv58ccfqa6uPu2xdujQIaZOnYpSqTzrVl3u7u689dZbLF++/JzSdOONN9LV1cV3330nr7vhhhv48ccf5aB54cKFXHzxxTz11FPyNh9//DGPP/449fX1/PTTT1x66aUUFBQQHx9/Tp9/vkYiaLbsXKUgWLHhAuWrr76af/7zn2YXKA8UGBiIv78/ZWVl7N+/Hz8/P5KSkkYsgDDWvNbU1NDQ0ICrqythYWFMmTLF4mtS4EQtRnl5OTU1Nbi7u5OYmEhQUJDZ1pZ3d3eTl5dHV1cXiYmJY97C4GS9vb3s27cPV1dXpk2bNqbniSRJHDt2jOjo6DELmE0hOTmZn3/+mZaWFvz8/MbkMxUKBTExMbi6unLo0CH6+vpMNl+8g4MD6enpREdHc/z4cbZu3UpiYiJRUVFm1RRaoVAQGBhIYGAgPT09VFRUsHfvXlxcXIiOjiYkJMRsryuno1AocHd3Jzk5maSkJFpbW6mpqWHnzp24uLgQFhZGaGjoiN0PDAYDVVVVFBUV4ebmxpw5c8xyvuzg4GAeeOABHnjgATmA/vTTT3nwwQetMoA+V0qlckS3O1sTJ07k4osvJi0tjcWLF3PJJZdw7bXXnnUrrLS0tCEB8+HDh1m7di05OTl0dHTIrZCqq6tJTk4+q/d99NFHueOOO/joo49YuHAh1113HTExMWd8XUFBAVddddWgdTNmzODHH3+U/8/JyWH37t28/PLL8jq9Xk9/fz99fX0cPXqU0NDQMQuYR4p55roFYZySJImcnBw2btzIp59+ajGB8slsbGyIi4sjLCyM4uJitm/fTmhoKAkJCefdpLO7u5uamhpqa2uRJInQ0FBmz55tlpmXcyVJEq2trZSVldHS0kJISAhZWVlmN7DXQGq1msLCQqqrq4mKimLq1Kkm71vd3t7O/v37CQ0NJTU1dcwDqrq6Onp6epg2bdqYfu5Yc3R0JCEhgWPHjjFv3rwxDRQDAgLIyspi37599Pb2MnHiRJMFqsaCmebmZvLy8qioqCA1NdWkrSxOxdXVlbS0NJKSkqipqaGkpIT8/HyioqKIiooyqxrTc6FQKPDz88PPz4/09HQaGhqorq4mPz8fPz8/wsLCzrvQUZIkamtrKSwsxNbWlokTJxIYGGh2v+1wThdAz5kzh1tuuYVrr73WJGNNmMrZFmSOdIGnra0tW7ZsYc+ePfz000+89dZbPPPMM+zfv5+oqKgzvv7kfs+9vb0sXryYxYsX88knn+Dn50d1dTWLFy9Go9GcdbrWrl3LjTfeyHfffccPP/zAmjVr+Oyzz4YExOejp6eH559/nquvvnrIc8aBIC2RaJ4tCGagsbGRTz75hI0bN1JeXs4111zDzTffzLx58ywmUD6d3t5eCgsLqa+vJzIyktjY2LO6aKrVaurq6qipqaG7u5ugoCDCwsIsfkAvI4PBQF1dHWVlZahUKiIjI81+/kljv+WioiK8vb1JSUkxi1rV+vp6jhw5QnJy8jnPPT4SdDodP//8M8nJyYSFhY3pZ49182w4cRxs27aNqKgok3zfKpWKffv24eDgYBYFNgaDgcrKSgoLC/Hy8iIlJcUs+zsbSZJEc3MzpaWldHR0EBYWJtfkWwOVSkVNTQ01NTX09/cTHBxMeHg43t7eZwx6JUmioaGBoqIitFotiYmJhIWFWUSwfCZ1dXX861//YuPGjZSWlnLNNdewfPly5s+fbxH3VEvs03wyvV5PREQEjz76KI8++uhptzUOBPbVV1/J6w4fPkxmZibV1dXyvebjjz/m5ptvJjs7m4yMjPNK1+9+9zt6e3v573//e9rthmue/bvf/Y4ffvhBbp49a9YsEhMTee+994Z9jx07drBgwQLRPFsQhLPT39/P119/zYcffsiWLVuYPXs2jz32GNdcc43VZFyMXFxcmDJlCrGxsRQVFbF161bCw8OJi4sbUvNsbH5dVVVFY2Mjnp6eREZGEhwcbPKM8UjRaDRUVlZSUVGBra0tMTExhIWFmX0BSVNTE3l5eQBkZmaaxRRjkiRRWlpKUVERmZmZJmt6WFZWhqOjo0n72o4lGxsbUlJSyM7OJiwsbMzPTScnJ7Kysjh8+DA7d+5k+vTpYzYw2XBsbGyIjo4mNDSUoqIiduzYQUREBImJiWZZi6tQKAgICCAgIICuri7KysrYtm0b/v7+xMTEmOVAg+fCycmJ+Ph44uLi6OzspKamhgMHDmBnZ0dERARhYWFDMs4Gg4H6+nqKi4vRarXExsYSGRlpkU3YTyUkJEQO1o4ePcqHH37IjTfeiIODAzfddBPLly8nISHB1MkcFTY2NixZsuS0o2cvWbJkxAPm/fv38/PPP3PJJZfg7+/P/v37aWlpOe9RusPDw7G3t+ett97innvuIS8vjxdffPGc3kOlUrF69WquvfZaoqKiqK2t5eDBg1xzzTVnfO2DDz7IrFmzWLduHVdeeSWbN28e1DQb4LnnnmPp0qWEh4dz7bXXYmNjQ05ODnl5ebz00kvMnTuXOXPmcM011/D6668TGxtLYWEhCoWCJUuWnNO+jCVR0ywIY0iSJPbu3cvGjRv517/+hb+/P8uXL+fmm28mPDzc1MkbM93d3ZSUlFBfX09ISAhxcXFMmDCB6upqqqur0ev1hIWFERERYVUFCD09PZSXl1NdXY2XlxcxMTFm2ZTzZH19feTl5dHa2kpiYiKRkZFmUSth7ENcX1/P9OnTTdacvb+/n61btzJjxgx8fHzG/PNNUdMMJ77/PXv24OnpSUpKyph97kAGg4Fjx47R0NDAjBkzzKa7hnGE5c7OTlJSUiyiplKlUlFRUUFlZSXOzs7ExsYSHBxsFuf6SNDr9TQ2NlJZWUl7ezsBAQFERETg4+NDXV0dJSUlSJIkdy2ypmD5dLRaLZs3b2bjxo188803ZGRksHz5cn7729+abKT6U7HEeZoLCgp45JFHOHLkCN3d3URERPDAAw9w//33n/G1w9U0A3z66ac8/fTTNDQ0MHnyZJ566imuuOKKQTXNkZGR3Hrrraxdu3bI+2o0GpYvX87u3btpamrC19eXq6++mr/85S9n9b3+v//3/1izZg1tbW0sXLiQuXPnDplyavPmzbzwwgtkZ2djZ2dHYmIid9xxB3feeSdwojvVqlWr+O9//0tvb6885dTll19+xs8/H2L0bEGwEJWVlXz00Ud8+OGHtLa2csMNN7B8+XKmTZtm9hmp0WQcRKqlpQUAT09PYmNjCQoKspqMGpyYj7CkpITGxkaCg4OJiYkx6/7KRgaDgdLSUoqLiwkODiYlJcVsRgnW6/VyJmTGjBkmrWXMzs5Gp9ON6WjSA5kqaIb/Gy18wYIFYzZP7ckkSaKkpISSkhKmTZtmNnMoG5v55uXl4eTkRHp6utkE9aej0+morq6mvLwcg8FATEwMERERZt8S5lz09vZSVlYmF9La29sTFxdHdHS0Vd17zlVHR4fcfPvIkSMsW7aM5cuXs2TJErNo6TUSgQ+cuLdVV1ejVCpxc3MjPDzcqn73vr4+fHx8+OGHH5g3b56pk2MWRuLYsZ4jRBhTr7zyCgqFgocfflheN2/evEFzgyoUCu65555Br/vvf/9LfHw8CQkJfPvtt2Oc6rGlVCp5//33mT9/PvHx8Rw4cIA//vGPNDQ0sGHDBqZPnz5uA+a+vj4KCgrYu3cvSqWSmJgYoqOj6evro7i4mJqaGvR6vamTeUGMg3vt2bOHXbt24ejoyMKFC5kyZYpFBMzNzc1s27aNuro6ZsyYweTJk80mYNZqtezbtw+VSkVWVpZJA+auri5qa2vPesRSa+Ph4UFoaCj5+fkmS4NCoSA+Pp7U1FT27dtHfX29ydIykEKhIDg4mAULFuDt7c2vv/7KsWPH0Gq1pk7aaU2YMIHo6GguvvhiUlNTqampYcuWLRQVFZ3TQEPmytjSqaqqCi8vL+Lj4/H09CQ/P58DBw7Q0NAgj0Y83nh5eXHPPfewd+9ecnNzSUxM5L777iMkJISHH36Y7OxsrKGuzcbGhsjISNLS0sym5dRI2rZtGwsWLBAB8wgTNc3COTt48CDXX3897u7uzJ8/nzfeeAM4ETTHx8fzwgsvyNs6OzvLg6Go1WpiY2N5//33kSSJ2267jbKyMrPs73W+9Ho927ZtY+PGjWzatIn4+HhuueUWbrzxRgICAkydPJMyGAxy07jW1lYCAgKIjIzE399fLjzQ6/XU1NRQXl5Of38/4eHhREZGWlQTbeOcxSUlJfT09MgDJZlLwHkmKpWKvLw8mpubzXIanf7+fvbu3YujoyNTp041ee3Xnj17cHd3JzU11WRpMGVNM/xf8/SZM2eavDlnY2Mjhw4dIiUl5axGph1LSqWS3NxclEolKSkphIaGWkTBqXGciZKSEjo7O4mMjCQmJsasByw8mV6vp6GhgcrKSjo6OggNDSUmJmbQYG0qlYrq6mqqqqowGAzy/ceUhXLmwGAwsH37dj788EP+/e9/Ex0dzfLly7nxxhsJCgoa07SMVE2zuThd3uaHH35g9uzZY5iaEy699FJ27tw57HNPP/00Tz/99BinaGSI5tnCmOvp6WHy5Mm8/fbbvPTSS2RkZAwKmgf+f7Lu7m7S09M5dOgQAFOnTiU3N9csRt69UE1NTbz33nu8++679Pf38/vf/57ly5eTnp5u6qSZnFqtpqqqisrKSuBEP5vw8PDTXrQkSaKtrY3KykoaGhrw9vYmKiqKwMBAswrgBjKOhF1SUoJGoyE2NpaIiAizaNJ2NgwGA+Xl5RQWFhIUFERKSorZZUp6enrYu3cvPj4+ZGRkmPxYaG5u5tChQyxcuNCkhX+mDpoBCgsLaWlpISsry+SBYHt7O/v27SMqKorExESTp2cgSZKoq6sjLy8PNzc30tPTLeoe2N7eTklJCc3NzYSHhxMbG2uyZvlno7e3l8rKSqqrq7Gzs5PvP6c7X42FBBUVFTQ3N+Pv709UVBR+fn5mdSyZQk9PD5s2bWLjxo3s3LmTpUuXsnLlSi6++OIxuR5bW9BcWlp6yudCQkJMMjVTXV0dKpVq2Oe8vb1NXjB6vkTQLIy55cuX4+3tzfr164cEyfPmzeP48eNIkkRgYCDLli3j2WefHVRK+/zzz/PSSy+hUCh46aWXePzxx020JxdOkiR27NjBO++8w3/+8x/mzp3LypUrWbp0qcUESqNFkiQ6OjqoqKigvr4eHx8fOeg910yHWq2murqayspKdDodoaGhhIeHm03fQL1eT3V1tXzzs8RBZDo7Ozl69Cg6nY6JEyfi5+dn6iQN0dXVxZ49ewgPDyc5OdnkmVfj+W8cyM6UzCFo1mq1bN26lUmTJplsBPOBlEole/fuJSAggPT0dJMfLyfTarUUFhZSVVVFXFwccXFxJi8EOhcDB3MMDg4mLi7ObKbY0ul01NfXU11dTUdHBwEBAURFReHr63vOx0FfXx+VlZVUVVVhb29PVFSUSUaLN0eVlZW8++67vPfee7i7u3P33XezYsWKUR0M0dqCZmHsiKBZGFOfffYZL7/8MgcPHsTR0XFI0Pzuu+8SERFBcHAwubm5PPHEE1x00UVs2rRp0Pt0dXVhY2NjUaXrA3V2dvLhhx/yzjvv0NTUxIoVK7j77rtNnnE2B3q9nrq6OsrLy+nt7SUsLIyoqKgR+a2Ntc/V1dXU19fj6upKWFgYoaGhJmn6bAyWi4uLsbOzIz4+3uJGmtXpdBQVFVFRUUFMTAzx8fFmGewbaw6NwYU5qK2t5fjx4yxcuNDk35k5BM0A5eXlVFZWMn/+fLMIUvv6+tizZw9eXl5MmjTJLM/Njo4Ojh49iiRJZGRkWFwtTm9vL6WlpVRXVxMYGEhCQoJJgueT7w8uLi6Eh4eP2P1Br9dTX19PRUUF3d3d8r3NXAoKTEmj0bBp0yY2bNjAgQMHuO6661i5cuWojNtiDHwiIyNNUgsrWC6VSkVlZaUImoXRV1NTQ2ZmJlu2bJGbHJ+pOfYvv/zCxRdfTGlpKTExMWOY2tFx+PBhNmzYwKeffkp6ejorV67kuuuuExduTmROKyoqqKqqwtHRkaioKEJDQ0ctA6/Vaqmvr6empob29nb8/PwICQkhKCho1IOGk4PlhIQEgoODzSJIOBfNzc3k5OTg4OBARkaG2Wb+WlpaOHDgAMnJyWbTR9VgMPDzzz8THx9PRESEqZNjNkGzuX0v8H994I1zxZu6gGM4BoOBsrIyioqKCAsLIzk52eJqMlUqFcXFxWMaPEuSJA/EV1dXh8FgICQkhPDw8FEdbNHYiqqurs4iug6NpePHj/POO+/w4YcfEhUVxcqVK/n9738/YuOS6PV6iouL8ff3N8n0foLlamtro7m5+YIqB0TQLJyVr776iquuumrQgabX61EoFNjY2KBWq4cchL29vbi6uvLjjz+yePHisU7yiOjr6+Ozzz7jnXfeIT8/n9///vesXLlSngdvPDOW7JeVldHc3ExAQADR0dH4+PiMaQDZ19dHXV0ddXV1KJVKAgICCAkJISAgYEQHiTIGyyUlJUyYMMFig2W1Ws3x48dpaGggKSmJqKgos90H46BO6enpZjWPubnVqJpL0AzmVQNvpNFo2Lt3L3Z2dlx00UUmHzzuVHp6esjJyaGnp4f09PQxH2RpJAwMnoOCgoiPjx/R4FmSJJRKJfX19dTW1tLf309QUBChoaH4+fmNaeBq7DpUUVGBJElER0cTERFhVYObnq/e3l7++c9/smHDBkpLS7nppptYuXIlaWlpF/zeDQ0NdHZ24u/vj7Ozs1lcgwXzJUkSfX19NDc34+npeUHXVRE0C2dFqVRSVVU1aN2KFStITEzkiSeeGHbk2N27d5OVlUVOTo7FDYhVWFjIO++8w8aNGwkNDWXlypXcdNNNZlsbN5YMBgP19fWUlZXR29tLZGQkUVFRZlHjrlQqqa2tpb6+nr6+Pvz9/QkODiYwMPC8gwnjfI7FxcUWHSxLkkR9fT25ubl4e3uTnp5uFr/ZqdTV1ZGdnc3kyZMJDg42dXJkOp2OrVu3kp6ebjbpMqegWZIktm/fTnh4uFm1MNJqtezfvx9Jkpg+fbrJv6dTkSSJmpoa8vLy8PPzIz093WJG3h+or6+PkpISOXhOSEg47246xhrl+vp6GhoaUKlU+Pv7j0rh6Pmmr7GxkbKyMjo7O+Vj35wHSBsrkiRx8OBBNmzYwGeffcaUKVNYuXIl11577Xkf18bvu7Ozc2QTK1g1T0/P8xpXZyARNAvnbWDz7LKyMv75z39y2WWX4ePjQ25uLo888gihoaHs2LHD1Ek9K1qtlq+++ooNGzawZ88errnmGlauXMmsWbMsLkAaDVqtlqqqKsrLy1EoFMTExBAeHm7yDMtwjLURDQ0N1NfXo1Qq8fPzIygoiICAgLMKFg0GAzU1NRQVFWFra0tiYqJFBstwokYkJyeHtrY2Odgz5/2orq4mNzeXqVOnmt1UbaWlpdTW1jJ37lyz+Q7NKWiGEzVBOTk5LFy40KyuDzqdjoMHD6LRaJg5c6ZZfFenolaryc3NpbW1lfT0dEJCQkydpPNycvCcmJh4Vk11DQYDbW1tNDU10dDQgEajISAggODgYPz9/c3quBqoo6ODsrIyGhoaCAgIIDY21uL6qY+W9vZ2Nm7cyDvvvEN7ezu33XYbd999N9HR0ef1fnq93uznPBfMg52d3Yi0fBJBs3DeBgbNNTU13HTTTeTl5ckDQF111VX84Q9/MPva2dbWVjZs2MDbb7+Nk5MTd999N7fddptZjiBsCn19fZSVlVFVVYWHhwcxMTEEBQWZTcBwNnp6emhoaKCpqYn29nbc3d0JDAwkMDAQDw+PQftirJEtKCgAIDExkZCQEIva34Hq6urIzc3Fx8eHiRMnmn2tlTFgnjZtmtmdg8YRoidPnmxWwby5Bc2SJPHrr7/KoyqbE71ez6FDh+jv72fGjBlm35TWeP76+vpabK0znLiPFBcXU1NTQ3h4OPHx8UMKLzUaDc3NzTQ2NtLc3IyNjQ0BAQEEBQXh5+dnNs39z0ZfXx/l5eVUVVXh5uYm3zdFv+cT14dffvmFDRs28N///peFCxfy2GOPsWDBAou9zwrjgwiahXGrpKSE9evX88EHHzBz5kweffRRlixZIm5q/7+Ojg5KS0tpbGwkMDCQmJgYqygx12g0NDU1yRkzW1tb/P398ff3R6FQUFJSglqtJiEhgfDwcIs9Hk6uqTL32mWAqqoqjh07ZpYBM0BRURHNzc1mMRfxQOYWNMP/zWG9aNEis0mTkTFwVqlUzJw50+wD5/7+fnJzc2lra2PixIlm0y3gfPT09FBQUEBTUxORkZEEBATQ3t5OS0sL7e3tuLm5yQWanp6eZnWenQ+tVkt1dTVlZWUoFAqio6MJDw83u3PCVOrr6+VKi/DwcB577DF++9vfiu9HMEsiaBbGnT179rBu3Tq+++47rrvuOh577DEmTZpk6mSZBUmSaG5upqSkhM7OTiIiIoiOjrbavlnGJoDV1dU0NDSg1+txcnIiNDSUgIAAvLy8LDJorq+vJycnBx8fH9LT0y1iPktjwDx9+nR8fX1NnZwhtFotW7ZsYerUqWYX0Jtj0CxJErt378bPz4+EhARTJ2cIg8HAwYMHLSZwliRJrnX29/cnLS3NImudVSoVra2t1NbW0tLSgiRJuLm5ERkZSWBgIM7OzqZO4qgwGAw0NDRQWlpKT08PUVFRxMTEWORvOBp6e3vZuHEjr7/+Omq1moceeog777wTDw8PUydNEGQiaBbGBb1ez9dff826devIy8vj7rvv5sEHHyQsLMzUSTMLkiTR0NBAcXExKpWKmJgYoqKizCYDPlqUSiUFBQU0NzcTHR1NWFgYHR0dNDc309LSgl6vx9fXF19fX/z8/HB3dzfrmg+tVktubi5NTU1yP0hzTq+RuQfMcKKWuaWlhaysLFMnZQhzDJrhxHRhBw8eNMvaZrC8wBlO1Drn5OTQ0dFBRkYGgYGBpk7SaWk0GlpbW2ltbaWlpYWenh48PT3l1j06nY7CwkJUKhUJCQlERERYZEHl2TLOOlFSUkJbWxsRERHExsaa9aCMY+nkvNpdd93FQw89JPJqglkQQbNg1fr6+vjggw/k0suHH36YO+64Q5Re/v8MBgO1tbWUlJSg0+mIjY0lIiLCbAdZGSkqlYrCwkJqa2sJDw8nISFhSG2sJEl0d3fT0tIiZ/psbW3x8fHBx8cHX19fswqiW1tbOXLkCG5ubkyaNMkiapfBMgJmc65lBvMNmiVJYteuXQQEBBAfH2/q5AzLYDBw6NAh+vr6LCZwNo6wfezYMUJDQ0lJSTGba7ZGo6GtrY22tjZaW1vp6urCzc1NLnj08fEZ8h0bC20LCgqQJImkpCSL6E5yoTo7OykuLqapqYnQ0FDi4uJGbD5ja7Bnzx5ee+01vv32W9EqUDALImgWrFJTUxN/+9vf5H4yq1at4vrrrzerDKUp6fV6qqqqKC0txcbGhri4OMLCwqy6hB9OjJ5bVlZGSUkJAQEBJCcnn3XTc4PBQGdnJ62trbS1tdHe3g4gB9E+Pj54eHiM+WA1BoOBgoICKioqSE5ONut5l09WW1vL0aNHzTpgBuSMrbn1ZTYy16AZzLtvs5GxxlmtVjNjxgyzTefJent7OXLkCBqNhilTpuDp6TnmaVCpVLS3t8vXRaVSiaurK76+vnLh4tkW4BlnLCgoKMDFxYWUlBSrGEfjTJRKJSUlJdTV1REUFERcXJwo2B+gtLSU9evX8/777zNz5kxWrVrF4sWLzfJaLFg3ETQLVqWgoIDXX3+djz/+mPnz54sRGU+i1WqpqKigvLwcBwcH4uPjx0WJvrFmpqCgACcnJ1JTUy84M2YwGOju7pZrVNrb29HpdHh6euLt7Y2Xlxfe3t6jWuOrVCo5fPgwAJMnTzb7keoHqq+v58iRI1x00UX4+/ubOjmnpNPp2LJlC1OmTDHbdJpz0CxJEjt37pSDAXOl1+vZv38/BoOB6dOnm03N7ZkYDAZKSkooKSkhPj6euLi4UbueGwwGurq6aG9vp6Ojg/b2dlQqFR4eHoMKDy+0n65Op6O0tJTS0lICAwNJTk622r7OA/X19VFaWkpVVRV+fn7Ex8ePi0KDs2Wc6eStt94iICCAxx57jN/97neiX7gwZkTQLFg84/Qm69atY8uWLdx44408+uijpKammjppZkOr1VJeXk5ZWRmurq7Ex8cTEBBg9cEynLjR5uXlodFoSE5OHrV+vpIk0dvbOyhD2d3djbOzM15eXnh6euLl5YWHh8cFZ8glSaKiooL8/HyioqJISkqyqFYCTU1NHDx4kClTphAUFGTq5JxWSUkJDQ0NzJ4922zPF3MOmuHE733kyBEWLVpk1sGoTqdj37592NjYMG3aNIua4qijo4PDhw/j6OjI5MmTLzjIlCSJvr4+Ojo66OzspKOjg66uLmxsbAYVCnp5eY3ab6pSqSgoKKC+vp7o6Gji4uLM8vgeaf39/ZSVlVFZWYmnpyeJiYn4+PiYOllmo7+/n48++ojXXnuNrq4uHnzwQe655x68vLxMnTTByomgWbBYkiTx448/8sILL1BYWMjKlSu5//77LXo6jpFmrFkuLS3F3d2dhIQEfH19zTbzP5J6eno4fvw4ra2txMXFERMTM+aZYK1WOyjT2dHRgVqtxt3dHU9PTzw9PfHw8DinZt1qtZrs7Gy6u7uZPHmyWTdrHk5LSwv79+9n0qRJhISEmDo5p6XX69myZYvZD7hk7kGzJEns2LGDsLAwYmJiTJ2c09JqtezduxcHBwemTp1qUYVROp2O48ePU1tbS0ZGxlmfX5IkoVKp6OrqorOzU160Wi0eHh5ygZ+Xlxeurq5jfv/o6Ojg+PHj9PT0kJiYaNFTAZ4LjUYjF3Z7eXmRmJgoap4HMBgMfP/996xbt47Dhw9z//338+ijj5rluBOCdRBBs2BxJEni22+/5YUXXqCyspJVq1Zx77334ubmZuqkmQ2dTicHyy4uLiQmJuLn5zcugmWtVktRUREVFRWnHOTLlFQqlRxId3Z20tXVhVarxdXVVQ6iPT09cXd3HxIAtba2cvjwYby8vMjIyLCIQYsGam9vZ+/evaSlpREeHm7q5JxRRUUFlZWVzJs3z6zPHXMPmuFEc/y8vDwWLlxo9gGPRqNhz549uLq6MmXKFLP+7YfT0NBAdnY2ISEhpKamDiqQM7aIMQbIXV1ddHV1odFocHNzkwvzvLy8cHd3N5vaduNgYfn5+djY2JCWljZugiONRkNpaSnl5eX4+PiQmJgoalVPsnfvXl588UV+/fVXVq5cyapVqwgICDB1sgQrI4JmwWIYDAa+/vprXnzxRerr61m9ejX33HOP1c4hfD50Oh2VlZWUlJTg7OxMUlLSuAmWjf2W8/PzcXNzIy0tzSL6+J5cy2PMxPb39+Pk5ISHhwfu7u709PTQ2NhISkqKRQ32ZaRUKtm1axfx8fFmX9sIJ643P//8M0lJSYSGhpo6OadlCUGzJEn8/PPPxMfHW0SBiVqtZufOnfKcyJZ2vvX19XHgwAG0Wi2hoaGo1Wq6urpQKpXy3MgDW7q4u7ubddN5I4PBQHl5OUVFRfj7+5OamjpupmtSq9WUlpZSUVGBr68viYmJJhn8zZwdPHiQF198ka1bt3LXXXfx+OOPi9aHwogRQbNg9gwGA19++SUvvvgibW1tPPHEE9x5553j5kZ5NvR6vRwsOzo6kpiYOG76LMOJqTuOHTtGX18fqampVjG4mVqtlgcaq6ysRKvVIkkSCoUCNzc3eXF3d8fNzQ1nZ2ez3ee+vj527txJeHg4SUlJpk7OWamtraWgoICLL77Y7GtGLSFoBuQR+y1lcEbjcRsREUFiYqKpk3NKWq0WpVIpL93d3SiVSvr7+5kwYQI6nY6AgABCQ0Nxd3fH1dXV7I/pM+nv7+f48eM0NDTIBXHmUis+2vr7+yktLaWyshI/Pz8SExPFaNsnyc7O5sUXX+SHH37g9ttv54knnhBzPQsXTATNgtkyGAxs2rSJ5557jvb2dh566CEefvhhESwPYDAYqK6upqioCHt7exITEwkMDLSIDOlI0Gg0FBQUUFNTQ3R0NPHx8RZRW3K2mpubOXLkCH5+fqSnp2Nra0tvb++QzHFPTw82Nja4urri6uqKm5ub/NjV1dWkmUm1Ws2uXbvw9fUlPT3dIo5NSZLYvn07kZGRREVFmTo5Z2QpQbPBYGDLli2kpaVZTO1Pd3c3u3btIikpyaTHgiRJ9Pf309PTI5/zxkWlUmFvby8XoA0sTLO3t5cHYgsICCA9Pd2qrpFtbW3k5uai1+tJS0sbV01y+/v7KSkpobKyksDAQBITE0U3tZMcOHCA5557ju3bt3PbbbfxzDPPmP1YGoL5EkGzYHYkSeLrr79mzZo1tLW18eSTT7JgwQJqamqws7MjLi6OkJAQiy8pvxAD+3cBJCcnExQUZBEByUiQJImqqioKCgrw9PQkLS0NV1dXUydrxEiSRGFhIWVlZXL/39P9tgaDYVAmemCmWqfT4eTkhKurKy4uLvJf4zKa55FOp2P37t04OzuTmZlpMcdnU1MT2dnZLFq0yCJqrywlaIYTc67W19czZ84cUyflrLW3t7Nnz55RH7xOkiQ0Gg29vb309PTQ29srP+7p6UGv1+Ps7DykYMzNze2M0+6oVCqOHDlCf38/U6dOtYiuK2dLkiQqKyspKCjAx8eH1NTUcdVtS6VSUVxcTHV1tVmO42EKvb29lJSUUFNTQ0BAADqdjvXr1/Ptt99y11138eSTT5r9zA2C+RFBs2A2JEniu+++47nnnqOhoYGnn36aO++8U7746/V6ampqKCkpASAuLo6wsDCLyNSOpNbWVvLz8+nr6xtXI4kadXZ2kpOTg0ajITU11epq1jUaDYcPH6a3t5eLLrrogjK3A2unBmbAjY8NBgPOzs64uLgM+evi4nJBAZjBYGDfvn1IksT06dMt6jzds2cPPj4+JCQkmDopZ8WSgmatVstPP/3EjBkzLGokYOM0adOmTbugAagMBgMqlYre3l76+voG/e3t7UWn0+Hg4DCocMsYHLu4uFzQeWQsjCsvLz+n0bUthVqtpqCggNraWmJjY4mLi7Oo686FUiqVFBQU0NzcTExMDLGxsWZ/PRhp3d3dlJSUUF9fT3BwMHFxcYPuoTk5Oaxdu5bNmzezcuVKnnjiCfz9/U2YYsGSiKBZMDlJkti8eTPPPfcclZWVPPnkk6xcufKUzbANBgN1dXWUlJSg0WiIiooiMjLS6ie47+7uJj8/n7a2NmJjY4mJibGqZnZnotVqKSwspKqqymozRJ2dnRw4cABPT08mTZo0qhke4wBkp8q8a7Va7OzscHJywtnZGWdnZ/mx8a+9vf2wBRaSJHH06FE6OzvJysqyqIxbV1cXO3fuZNGiRRZzTbGkoBng+PHjcqGQJamurubYsWPMnj37lIVZer0elUqFSqWir6+Pvr4++bFxPSCfU8MVWI32b9jY2Mjhw4eJiIggOTnZ6gpdOzo6yMnJQafTkZGRYXHT8l2o9vZ28vPzUSqVxMfHExUVZXW/8UCSJNHS0kJ5eTktLS2Eh4cTGxt72tYGhw8fZu3atWzbto3777+fVatWjbvjRDh3ImgWTGrfvn2sWrWKwsJCHn/8ce67776zblYlSRJNTU2Ul5fT1tZGaGgoMTExVtXsDE40vSosLKS2tpbIyEji4+MtJjM/UhoaGjh27BjOzs5MnDjRKvttVVVVcezYMRISEoiNjTV57blWq6W3t3dQhn9gEKDRaLCxscHJyQknJyccHR3lx+3t7TQ3NzNjxgw8PDxMvi/n4vDhw0yYMIGJEyeaOilnzdKCZpVKxdatW5k/f75FdavQ6XRyTWZCQsKgAFmlUtHf349arUahUAxbyGRcHB0dTR7E9PT0cPDgQezs7MjMzLS65rwGg4GKigoKCgoICQkhJSXF4qbouxDG/FF+fj56vZ6kpCRCQkIs6lp8JsbWh+Xl5ajVankMinM5lg8cOMCaNWvYtWsXq1ev5rHHHhtXTfuFcyOCZsEkysrKeOqpp/juu+9YtWoVq1atuqBAqLu7m/Lycmpra/Hy8iImJsbiR4/WarUUFxdTUVFBUFAQiYmJ4+5irlKpOHbsGK2traSkpJyxb68l0uv1HDt2jIaGBjIzMy1m7lGdTjckYDDOQd3d3S2P2qtQKHBwcMDR0XHQ4uDgIK83PjZ1ywFLDeYsLWiGE4UTdnZ2pKenmzQdkiSh1WrlgNe49Pf3D1mMx7Mx4PX395eD4oGLg4ODRVyndDodOTk5tLS0MHXqVHx8fEydpBHX19dHbm4uHR0dpKamEhoaahG/zUgxTsVYUFCAg4MDKSkpFnOPORWVSkVFRQVVVVU4OjoSHR1NaGjoBd0/du3axapVq6iurubFF1/k1ltvNfn9SDA/ImgWxlRbWxsvvfQS77zzDr///e954YUXRnQUVbVaTVVVFRUVFdjY2BAREUF4eLhFlaIbDAaqqqooLCzE3d2dlJSUcTcXoyRJci1BYGAgqampVlm7rlKp2L9/PzY2NkydOtXiR4ZvaWlh//79cr9PvV4/bPAxMDAxPgaws7PD3t5eDqKNjwf+HbjY2tqOaAbY2KRx2rRpI/aeY8ESg+bOzk527drFJZdcMqI1gAaDAa1Wi0ajGbSo1Wr578mPJUnC1tZ2UAHOcAU9jo6O2NvbI0kS+/btQ6FQMG3aNJPXGl8I47U2Pz+f5ORki5wD/kyMA2fm5ubi7u7OxIkTx10BtF6vp7y8nOLiYnx9fUlJSbGogkFjE+zKykqamprw9/cnOjoaX1/fETteJUniiy++4KmnnsLFxYVXX32VxYsXW935IJw/ETQLY6K/v5+//e1vvPzyy0yfPp1XX32VtLS0Ufs8g8FAY2MjlZWVtLW1ERAQQGRkJH5+fmZ9AWxpaSEvLw+9Xk9qaqrF15afj+7ubo4ePYparWbixIlWO0hHe3s7Bw4cIDAwkLS0NIsv1e7u7mbnzp3yaN/nQpKkQbV8wwU3A/9qtVoAbGxs5EDb3t4eOzs7+X/j45OXCRMmyH8Hnlt6vZ6ffvrJomr7jSwxaAbYuXMnQUFBxMbGyuskSZIDX51ON+ivMRg2Ph64zrjodDoAbG1tz6oQxhgon+v4EFqtll27duHl5cXEiRMt/jo98HqUnp5u0QUBp6LVasnPz6empob4+HhiY2Otcj9PR61WU1hYSHV1tTxNozlfM/r7+6murqaqqgq9Xk94eDgRERGjWuihVqvZsGEDL7zwAlOmTOEvf/kLGRkZo/Z5guUQQbMwqgwGA5999hlPP/00np6erFu3joULF45pGnp7e6mqqqK6uhpbW1uzrH3u7e0lLy+P1tZWEhISiI6OHnc3c4PBQElJCSUlJURFRZGQkGC1A51VV1eTm5trNTU7arWaX3/9lbCwMBITE0f984xNak+uTTw5mBoYVA0MwIyMgdWECRMwGAyo1Wr8/PyYMGECtra2TJgwQX5sXE7+37jY2NgM+qtQKMbsdx3roFmSJPR6PQaDYdi/er0enU4nPx64zrjo9Xp5KiVXV1d5vVarxZgtMRaKGAs6TlUoYvz/5FYIo02lUrFjxw7i4uKIiYkZ9c8bbcaWL7a2tlx00UVW2boHThQQHD16FBsbGyZPnmx146Ccja6uLvLy8lAqlSQlJZlV1ydjrXJVVRWNjY34+PgQGRlJYGDgmOaLOjo6+OMf/8jf/vY3fvvb3/LSSy8RGho6Zp8vmB8RNAujZseOHaxatYrGxkZeeuklbrrpJpPWphkMBhoaGqiqqqK1tRU/Pz/Cw8MJDAw0WboG9lsODQ0lKSnJajMqp9PV1UV2djYGg4FJkybh5eVl6iSNCoPBQH5+PtXV1WRmZlpFLbrBYGDPnj04ODhYxFzMkiQNCtCMf3NycvD19cXT01MO6gYGeKcKBAcGiyczBtE2NjZyX9iBi3GdMcAe+HjgAgz6e/J3bDAYqKmpISwsTM5UGm/tkiQNemz8azAY5OeMi3GdwWAYspy8fiCFQjGowOBUhQvGQoiBzxUUFBAZGYm/v/+gVgDGbcxdR0cHu3fvZurUqQQEBJg6ORdMp9ORnZ1NR0cH06ZNw8PDw9RJGhV6vZ6ioiLKy8uJi4sjLi5u3BVUG5utHz9+HDs7O1JTU006grRSqaSmpoaamhokSSIsLIzIyEiTN6WvqKjgmWee4euvv+bhhx/miSeeGJcFLYIImoVRUFhYyBNPPMG2bdt46qmnePjhh82ur2ZfXx+1tbVUV1ejVqsJCQkhPDwcLy+vMcn0S5JEdXU1BQUFuLq6kpqaOu76LcPg2uXo6GgSEhIsIqN8PrRaLYcOHaKvr49p06ZZVH+yUzFOLdXV1UVWVpbFtgxobW3lwIEDXHLJJee9DwODypNrX88UjA4XuA5cjO9v/HvybdtgMFBaWjqouenpgu3hAvSTg/fhgvuTa9MHbnO+iouLaWlpYdasWef9HqZWW1tLTk7OaaeisiSSJFFcXExJSQmTJ08e0XFHzE1HRwfZ2dnY2NgwadIkqy0kOB29Xk9ZWRnFxcUEBASQkpKCs7PzmHy2RqOhrq6O6upquru7CQwMJDw8HD8/P7MrxDh48CCrV68mPz+ftWvXcuedd5p103Zh5ImgWRgxTU1NrF27lg8++IDbb7+d5557zuxr0iRJoqOjg5qaGurq6rC3tyc0NJSQkJBRm9aoo6OD3NxcNBoNKSkpBAUFmX3t3GgYL7XLcGJ6l3379uHq6sqUKVOs5kZbWlpKaWkpc+fONbuCsXNx4MABXFxcSElJMXVSzoul9mmGE5nmzZs3M3fuXIsOOI1TUc2ZM8dqWgs1NDRw+PBh4uLiiI+Pt9r7lKh1PqG/v5+CggLq6uqIjY0lLi5uVAqxdTodzc3N1NbW0tTUhIeHB+Hh4QQHB5v9tGCSJPHNN9/w+OOPI0kSr776KldccYXVnhvCYCJoFi6YSqXi9ddf589//jMXX3wxr7zyCgkJCaZO1jnT6/U0NTXJF3JXV1dCQkIICQkZkeZBAwchGc0bkrkbWLscExNDfHy8VX8PbW1tHDhwgLCwMFJSUqzm5trU1MTBgweZNWuWRRd4GKeZWrBggcmbAZ4vSw6aAY4cOcKECRNMPv3UhZAkiUOHDqFWq5k5c6bVBF1dXV3s378fX19fMjIyrGa/hiNqnU/o6OggJycHnU5Henr6iFR+6PV6WlpaqKuro6GhAUdHR0JCQggLC7PIVldarZZ//OMfrF27loSEBF5//XUyMzNNnSxhlImgWbggP/zwA/fffz/e3t68/vrrzJ4929RJGhFarZbGxkbq6upobm7Gw8OD0NBQgoODz7lGTZIkamtrOX78OO7u7qSnp1vkTWIkKJVKDh8+jMFgYPLkyVbfJL2uro7s7GxSUlKIiooydXJGTE9PDzt27GDixIkWPzBKUVERHR0dTJ8+3dRJOW+WHjR3dHSwZ88eFi9ebLFN/OFEDdquXbvw9va26AKAkxkHCLOzs2Pq1KlmXxt4IfR6PcXFxZSVlZGQkEBsbKzVFHSei4HTPvr7+5OamnrOeR+DwUBra6scKE+YMEGuiPDw8LCK71WpVPLnP/+Z9evXs3z5cl5++WWLLkQWTk8EzcJ5qamp4ZFHHuHnn3/mT3/6E3feeafV1hZqNBoaGhqora2lra0NDw8PgoKCCA4OPmPwq1Qqyc3NRalUkpaWRnBwsFXcKM6VJElUVlZy/PhxIiMjSUpKstrjBU7sr7E2PTMz0yoGCDLS6XT8+uuvckbKkhkMBrZs2cLEiRMJDAw0dXLOm6UHzZIksWPHDiIjI4mMjDR1ci5Ib28vO3bsIDU19ZynXjNnOp2OQ4cO0dvby4wZM8asz6uptLe3c+TIEZycnJg0aZLV7++p9Pf3c/z4cRobG0lMTCQqKuq0rQ30ej3Nzc00NDTQ2NiIjY2NHCiP1ZgxplBaWsr999/PkSNHWLduHTfffLPV7ut4JoJm4ZxotVrefPNNnn/+ea655hpeffVVs++3PJLUajWNjY00NDTQ0tKCi4sLQUFBBAUFDSo51el0FBcXU15eTkREBImJiRaZmR0JarWa7Oxsurq6mDx5ssXNgXuuDAYDubm5NDU1MW3aNKuqTTc2QdVoNMyYMcPim2rW19eTl5fHokWLLDqDY+lBM0BVVRXl5eXMmzfPon8LgObmZg4cOEBWVpbVnf/Hjh2jvr6eadOmWX2Nmk6nIy8vj7q6OqtoVXMhWlpayM3NxdbWlvT0dLy9veXnjC3zGhoaaG5uxtHRUc4XWXOgfDJJkti0aRMPPfQQMTExvP322xY7ToYwPBE0C2dt586drFy5EkmS2LBhA3PmzDF1kkxKq9UOKlG1t7cnMDAQe3t7qqurcXR0ZOLEieO2XxRAY2MjR48excfHh4kTJ1p1sz74vxGy+/v7mT59ukUPjjWckpISKioqmDt3rlUMdrRnzx58fX2Jj483dVIuiDUEzTqdjs2bNzNjxoxBGXJLZW3nipEkSZSXl1NQUMCUKVMICgoydZJGXUNDA0ePHsXf35/09HSLPcculF6vp7S0lJKSEgICAvDw8KC1tZXW1lbc3NzkFnhubm7jJlAejlKp5Pnnn+ftt9/mgQce4Nlnnx23XfKsjWVXEwhjoqWlhVtvvZVLL72U5cuXc/To0XEfMAPY2dkREhJCZmYml156KcnJyTQ3N1NUVIRarcbBwYGOjg5UKpWpkzrmdDodOTk5HD58mJSUFDIzM60+YO7v72f37t1IkkRWVpbVBczGY/uiiy6yiiCgr6+PtrY2q2pCa8kmTJhAWFgYVVVVpk7KiIiNjcXLy4tDhw4NmdfakikUCmJiYpg8eTKHDx+msrLS1EkadUFBQcyfPx+NRsO2bdtobW01dZLGnMFgoKOjA51Oh6OjI/X19RQWFuLo6MjFF1/M/PnzSUxMxN3dfVwHzABubm6sW7eO/fv3s3v3bpKTk/nPf/4zZKpAwfKImmbhlAwGA//7v//LU089xbx583jjjTdEBnMYkiRRV1fHsWPH8PHxIS0tTW6u1NTUREdHB+7u7gQEBODv74+Xl5fFN2s9na6uLg4dOoS9vT1TpkwZF33Bent72bt3L56enkyePNnqfl+VSsX27dtJSUmxmmtAQUEB3d3dTJs2zdRJuWDWUNMMJ64dO3fuZPHixRa9H0bG/v9BQUEkJSWZOjkjrq2tjf3798uzIFh7sGQcHCs/P5/o6GgSExOt7lo/kEqloqWlhebmZpqamrC1tcXf35/AwEB8fX1pbm6W8z3p6ek4OjqaOslmx2Aw8OGHH7J69WqmTZvGX//6V6Kjo02dLOE8iaBZGNaRI0dYuXIlLS0tvPXWW1x++eWmTpJZUqlU5Obm0tHRQXp6OsHBwUO2UavV8k2npaUFg8GAr68v/v7++Pv7W+w0NycbONiXtc/rOVBXVxd79+4lJCSE1NRUq9tng8HA7t27cXV1ZdKkSaZOzoiwlgHAjKwlaAbYvn27VQwIZtTd3c2vv/7K1KlTrWpAQCPj9S84OJi0tDSru/4Np7u7m0OHDmFnZ0dmZqbVtCrS6/W0tbXR3NxMS0sL3d3deHp6EhAQQEBAAJ6enkN+X7VazbFjx2hubiY1NZWwsLBxcQycq/b2dp566ik+/vhjnnrqKVavXm0VLbbGGxE0C4N0dXXx7LPP8o9//INVq1bx1FNPWc0NYSRJkkR1dTV5eXkEBgaSlpZ2Vs2PJUmiq6tLvim1tbXh7OyMn58f/v7++Pj4WGQzZq1Wy9GjR2lrayMzMxNfX19TJ2lMtLa2sn//fuLi4oiLi7PKzMLx48dpbm5m9uzZFj0d0ECNjY3k5ORwySWXWMVvZk1Bc2VlJVVVVcydO9fUSRkxNTU15OXlMW/ePKu8n1p7S5vh6HQ6jh07RmNjI5MnT7bIAhFJkuju7qalpYWWlhZaW1uxt7eXC/T9/PzOOj/S0NBATk4OHh4eTJw4cVy0MDsf+/btY+XKlfT19fH3v/+dhQsXmjpJwjkQQbMAnLh4fv755zz00EOkpaXxt7/9jYSEBFMnyyz19vZy9OhRenp6yMjIuKCbpVarHVSy29PTg4eHB35+fvj6+uLt7W32meDOzk4OHTqEs7MzU6ZMGTelpw0NDRw+fJi0tDQiIiJMnZxR0djYyOHDh5kzZw5ubm6mTs6I2b9/P+7u7lbTZNaagmatVsvmzZuZPXu2VQ2iePToUbq7u8nKyrLKoFKtVrN3717s7e2ZOnWqxR+HZ6umpobc3Fx5KkVz/m0lSaKnp4fW1la50F6v1+Pj4yMHyq6uruddkKjRaDh+/Dj19fUkJycTGRlpFYWSI02n07Fhwwb+8Ic/cOmll/Lmm29aZKHLeCSCZoHm5mbuvfdefv31V/7617/y29/+VlzohjFw1NCwsDCSk5NHPGPQ398v39BaW1tRqVR4enri6+srB9HmUts3sH9XfHy81da0DqempoacnBwmT548bJN8a9DX18f27dtJT0+3qqlW1Go1mzdv5uKLL7aarhHWFDTDie5BdnZ2pKWlmTopI0av1/Prr7/i5+dn8fObn4pWq+XAgQPo9XqmT59uka2mzodSqeTQoUPY2tqSmZlpNrWskiTR29tLW1ubnK/QarV4e3vLBfOenp4jHug3Nzdz9OhRnJ2dmTRpktVcZ0daQ0MDDz30EL/88gsbNmzguuuuM3WShDMQQfM49+WXX7Jy5UrmzJnDhg0brH4O3fOlUqk4cuQIfX19TJo0acyaH/f19Q0qFe7v78fDwwMfHx95MUXGRKvVkp2dTUdHB5mZmfj4+Ix5GkylsrKSvLw8LrroIqudo9xgMLBr1y65qZ01KSsro6GhgaysLFMnZcRYW9Dc0tLCoUOHWLx4sVnX3J2rnp4eduzYwZQpU6yiL/1w9Ho9hw4dQqVSMWPGjHHT8kiv18tzOk+ePNkkv6+x+1dbWxvt7e20tbWh1Wrx8vKSC969vLywtbUd9bTodDry8/Oprq4mLS2N8PDwcVOofq4+//xz7r33XhYuXMjf/va3cdO9zRKJoHmcam9v5/7772fz5s38/e9/F7XLp1FbW0tubi5BQUGkpqaaNFNqnCbHuPT09ODm5oaPjw/e3t54e3vj7Ow8qr9ld3c3Bw4cwMXFhcmTJ4+bTBGcCLgKCwuZPn26VRcUFBQU0NDQwNy5c8ckgzWWtm3bRkxMjNWMAg7WFzRLksSWLVtIS0uzunmAq6urOX78OPPnz7fa0YYNBgOHDx9GqVQyc+ZMq93P4dTV1XH06FF5dO3RvBfrdDo6OzvlALm9vR0Ab29vuVDd09PTpNfw5uZmjhw5gpeXFxkZGeMqv3AumpqauPvuu9m3bx//8z//w5VXXmnqJAnDEEHzOPTtt99y5513kpmZybvvvmt1mZKRotFoyM3NpaWlhYkTJ5plM1y1Wi3fMNva2ujq6sLe3h4vLy85iB7Jm2ZdXR3Z2dnExsaSkJAwrgpaiouLKS0tZcaMGXh5eZk6OaOmtbWVffv2MWfOHNzd3U2dnBFlnNJoyZIlZtPNYSRYW9AMUFhYSFdXl1VMCTaQJEkcPnwYjUbDjBkzrPYaajAY5NZIs2bNssoB0E5lYMHylClTRqQ1mCRJqFQq2tvb5aW7uxt7e/tBQbKHh4fZHVMajYacnBxaW1vJyMgQec5TkCSJjz/+mAcffJBly5bx5ptvWnVewxKJoHkc6erq4uGHH+Y///kPb7zxBsuXLze7i6u5aG5uJjs7Gw8PDzIyMiympFyv18slzx0dHbS3t6PRaPDw8JADaC8vL1xcXM7ptzcYDBQUFFBZWWnVTQuHI0kShYWFVFZWMnPmTKsanOhkGo2G7du3Exsba5VzSR47dgytVsvkyZNNnZQRZY1Bc29vLz///DOLFy+2utoprVbL9u3biY6OJiYmxtTJGTWSJJGTk0NzczOzZs0aV31btVotR44cobu7m4suuuic7xtarZauri46OjqG3MsHFoo7OTlZRD5OkiS51V5ISAgpKSlWc60aaXV1ddxxxx3k5uby3nvvsWTJElMnSfj/iaB5nPjpp5+4/fbbSU5O5h//+AdhYWGmTpJZ0uv15OfnU1VVRWpqKhERERZxQzoVSZLo6+uTb7qdnZ10dXVha2uLp6enHER7enqesiZArVZz6NAh1Go1F110Ea6urmO8F6YjSRL5+fnU1NQwc+ZMq6t5HUiSJA4dOoRer2fatGkWfdwPx2Aw8NNPPzFlyhSrG7vBGoNmgJ07dxISEmKVBTjt7e3s2bOHrKwsPD09TZ2cUSNJEnl5edTX1zNr1qxxd/8oLi6mpKSEiRMnnjLfpdfr6e7upqOjg87OTjo6Oujp6cHR0REvLy85SPbw8LD4FjJ9fX1kZ2fT19fHlClT8Pb2NnWSzJIkSbz33ns8+uij/Pa3v+W1116z6vyHpRBBs5VTKpWsXr2aTz75hHXr1nHXXXdZXWZ4pHR1dXHo0CHs7OyYPHmy1d7cDQbDkBu0UqnEwcEBLy8vPDw88PDwwNPTE5VKxaFDh/Dy8mLSpEkWf8M+F5IkUVBQQHV1NVlZWVZ7PBhVV1eTn5/P/Pnzra5mD070GTt69KjVzM08kLUGzRUVFdTU1DBnzhxTJ2VUFBUVUVtby7x586xu7ICBjIWPtbW1ZGVljasaZzhx7Tl8+DBhYWEkJibS09NDV1eXXIjd1dXFhAkT5AJs419LaeF2riRJkscHiYmJISEhwaoG/BtJVVVV3HbbbZSWlvL++++zYMECUydpXBs/OeBxaPv27axYsYLIyEhyc3OJiooydZLMkiRJVFZWcvz4cWJjY4mPj7fqC7iNjY1cy2xkHFDEeCOvr69HqVQC4OLigouLC83NzXh4eIz6QGPmwNgku7q6elzUjvT19XHs2DEyMzOtMmCGEwP6hYaGWv2xa01CQkLIy8ujt7fXKgOtuLg4mpqayM/Pt6rptU6mUChITk7GYDCwe/fucdNUW6vV0t3dTW9vL76+vlRWVlJeXo6dnR2enp54eHgQExODl5fXuLivGikUCmJjY/H39+fQoUO0traSmZk5rvq9n62IiAi2bNnChg0buOKKK7j11lv585//PC7OH3MkapqtkEql4sknn+S9997jT3/6E/fdd59VB4EXQqvVcvToUdra2sjMzBRD/fN/Nazl5eUkJSVhY2MjB9Td3d3Y2tri7u6Ou7s7Hh4e8mNrqoU29mGeNWsWbm5upk7OqJIkiT179uDi4kJGRoapkzMqdDodP/74I7Nnz7bKPunWWtMMsH//fjw9PUlISDB1UkZFT08P27dvZ/r06VZ//5EkiWPHjtHY2EhWVpbZzGd8oYzdoLq7u+X7pDFYdnBwkANkNzc3qqur6enpYfr06aK5LSeuzceOHaOhocFkU3VZitLSUlasWEFDQwMffPCBVU2baCmsJ5crACcy+9dffz0uLi5kZ2cTFxdn6iSZrY6ODg4dOoSrq6vVNkk9VzqdTh68ZO7cuUMCRoPBgFKplDMH9fX1FBQUoNFocHFxkQNpNzc3/j/2zjs8rupa+++Z3jRFM+qyui13W5Yt9xYMGELnBkhILhBKQihJCHwJ3AQCJCG5uUlIARIIaYQWQkLAFAeMbVxkq9pyl2X1rhmNNL3v7w9zTkYjjTRHlnRmNPv3PPs5dUZrtk7Z715rr52SkgK1Wp1wHTZnzpxBS0tLUghmAGhubobL5UJFRYXQpkwbvb29UKlUtJGagOTm5uLUqVOYN2/erPTEaTQaLFy4EHV1ddi6deus6/QIh2EYLFmyBIQQzuOcaMLZ5/Nx78DwEgwGkZKSAq1WC4PBgIKCAmi12lEh1jk5OTh9+jT27duHlStXIiMjQ6BfEh9IJBKUlZXBZDKhtrYW+fn5WLhwYcK1G2aCkpIS7NmzB7/61a9w6aWX4pFHHsHDDz9M62oGoZ5mAM899xyee+45tLa2AgAWLVqERx99FJdddhkAwOPx4Fvf+hZee+01eL1eXHrppXj22WdHPOzefvttPPjgg2AYBj/72c9wxRVXzPjvePnll/HVr34VX/va1/CDH/xgVr98LwRCCJqbm3Hq1CmUlpaipKRkVjbG+OJ2u3H48GFIJBJUVFTwmibD4/GM6GW32+1ceLdGo4FWq+WEtFarjdtQtMbGRpw7dw7r169PCoFlt9uxd+/eWe/lOnToEAwGw6z1Vs5mT3MwGMT7778/qxNmsdEeKpUKZWVlQpsz7RBC0NDQgP7+fmzYsCEuw3L9fj/3HmPfaTabDV6vFwqFgnunhXcU8xEvHR0dOHr0KBYuXIjCwsK4fB/ONA6HAzU1NWAYBitXrqQhyONQV1eHG264AcXFxXjppZeQnp4utElJARXNAN555x2IxWLMnTsXhBD8+c9/xk9/+lPU19dj0aJFuPvuu/Huu+/iT3/6E3Q6He69916IRCIcOHAAwPnswiUlJfjjH/8IQgi+/OUv49y5c1MyN18suN1u3H///fjnP/+Jv/zlL7j88stn5O8mIj6fD/X19RgeHsbKlStp5sZPsVqtOHz4MDIyMrBs2bIp6bkkhMDpdI4Q0TabDQ6HAyKRCBqNBhqNBikpKdxSrVYLlhCnubkZp0+fxvr162dlCG8khBB88sknMBqNWLx4sdDmTBs+nw87d+7E1q1bZ+3Y9NksmgGgtrYWCoUCixYtEtqUacPtduPjjz9Omin9CCE4cuQIBgcHsWHDBkEivQgh8Hq9sNvtcDgcI5YejwdyuXxEZy+7nKp7bHBwEFVVVcjKysKSJUuoxxDnO8lOnDiBjo4OlJWVITs7W2iT4habzYY77rgDBw4cwKuvvjprEybGE1Q0RyE1NRU//elP8V//9V9IS0vDK6+8gv/6r/8CcD4EesGCBaisrMSaNWtgs9mwdOlS1NTUAABWrVqFhoaGGQntZMOxU1JS8Nprr9GppMZhcHAQNTU10Ol0KCsrm7FOjXinq6sL9fX1WLBgAYqKiqa9xzsUCsHpdHJCOryxEgwGoVarOSGt0WigVquh0Wggl8unzTa213/dunVJ05HS1NSE1tZWbN26dVZn7m1ra0NLSwu2bNkitCnTxmwXzb29vTh27Bi2bds2qz1ybW1tOH36ND7zmc/Myv9jJIQQVFdXw+12Y926ddP2m4PBIJxOJxwOB7dk3zl+vx9KpXJE5y27nAkh73K5cPjwYchkMqxatYq2Sz6lu7sb9fX1mDNnDhYvXkw7FKJACMFvf/tbPPjggzRcewagY5ojCAaDeOONN+B0OrF27VrU1tbC7/dj27Zt3Dnz589HXl4eJ5q1Wi1uu+02ZGVlgWEY/OAHP5gRwcyGY99zzz148sknk+IlO1laW1tx/PhxzJ8/H8XFxbO64RUr4dM+rFy5csa8GyKRiOu9j7TH4/GMENNdXV1wOBxwu90Qi8WckGbFNCuopVLppP+nvb29OHr0KCoqKpJGMDscDpw+fRpr166d1YIZON/4ysnJEdoMygWQlpYGn88Hq9U6q+/RvLw8dHV14eTJk1i2bJnQ5kw7DMOgvLwchw8fRlVVFdasWTPp51EoFILL5Roljsd6f6jVaqSlpXHbQiaxVKlU2LBhA2pra7F//36sXbs2LsPVZ5rs7GzodDpUV1fjwIEDWLVq1aydgutCYBgGd999N9asWYPPfe5z+OSTT2i49jRCPc2fcuzYMaxduxYejwcajQavvPIKLr/8crzyyiu47bbb4PV6R5xfUVGBrVu34ic/+Qm3b3h4mBME04nL5cL999+Pt956i4ZjT0AwGOQyM65atWpWj9vkAyEEx48fR1dXF1avXg2DwSC0SeMSCATgcrm4RhBbnE4nfD4fJBIJJ6Iji0KhiCqozWYzDh06hBUrViRNGBg7fjIlJQVLly4V2pxpxev1YufOndi2bVvCJRziw2z3NAPnx/DJZLJZPZQAAJxOJ3bv3j3r8wyEEwgEcPDgQcjlcqxatSqqp4x9DzidzlHF7XaDYRioVKoREUrs+njvgXggFAqhoaEBfX19WLt2bVLk1IiFYDCII0eOYGBgIKk6tieDzWbDnXfeiX379uHVV1/F5s2bhTZp1kE9zZ9SWlqKI0eOYHh4GH//+99xyy23YO/evby+YybGQZ4+fRqf+9znoNVqudAVyti43W5UV1eDEIItW7bQ3ttPCQaDqKurw/DwMDZu3JgQyTYkEgk3tVUkfr9/VEPKarVyDSmRSASVSjWiqNVqBAIBNDQ0YPHixUkjmIHzIaBOp3NWZ8tm6enpgV6vn9WCOVnIycnBkSNHsGjRorgWPxeKWq3GggULcOTIEWzZsmVWTeUXDYlEgjVr1mD//v2orq5GYWEh3G43nE4nXC4XV7xeL8Ri8YhO0ezsbG5dqVQm7LUhEomwbNkyNDY2Yt++faioqEBaWprQZgmOWCzGihUr0NLSgoMHD2LRokUoKChI2P/zdKLVavHaa6/ht7/9LS6//HIarj0NzP6ncYzIZDKUlJQAAMrLy1FdXY1f/vKXuPHGG+Hz+TA0NDQic2dfX9+MJ+ug4dixY7FYUF1djYyMDCxdunTWh6DGis/nQ1VVFUKhEDZu3DgrptmSSqXQ6XRjdloFg8ERjS6Xy4WhoSF0dHRw2b1PnDiBlpYWqFQqKJVKbsmuT+dY6pnG7XbjxIkTWLlyZVI8P3p6epKqQ2Q2k5aWhkAggKGhobiPjLlQioqK0NXVhdOnT88qz3ogEIDb7Ybb7YbL5RpzabfbYTabYTAYuGniMjMzuc5OmUw2a57HkTAMg9LSUigUChw+fBhlZWV0aAnO10tRURG0Wi1qamowNDRE23VRCA/XvuGGG2i49hRDRXMUQqEQvF4vysvLIZVKsWvXLlx//fUAzs/j2t7ejrVr186ILeHh2K+//joNxx6H8OmkaI/kSNxuNyorK6FWq1FeXp4UHgyxWDxq/LTX68W+fftQXFyM0tJSTkyzDTer1Yquri643W54vV6IRCJORLNFoVCM2L6QMdUzybFjx5CZmZkUc4P6/X4MDAzM+hD0ZEEkEiEzMxPd3d2zXjQzDIPly5fjk08+wZw5cxIim38oFILH4+FE8VjF5/Nxz1M26kej0SAtLY3bDoVC2LdvHzIyMlBcXCz0zxKE/Px8KBQKLkka69BJdkwmEzZv3oyqqirs378fFRUVNIIwCmVlZaitrcWdd96J5cuX03DtKWL2t5pj4OGHH8Zll12GvLw82O12vPLKK9izZw927twJnU6H22+/HQ888ABSU1Oh1Wpx3333Ye3atVizZs2029ba2oqrrrqKhmPHQDAYxNGjR9Hf359UWZBjwWazobKykvO8J2u4TiAQwKFDh6DT6bgwz2heauD8NRXuBWEbhUNDQ1xDMBAIQCwWc0I6fBlZhKz33t5emM1mXHTRRYLZMJP09vZCq9UmxPADSmxkZ2fjxIkTWLhwYUJ0Ul0IWq0WRUVFOHr0KDZu3CjY7yWEwO/3w+PxjCjs85Bd93q9YBhm1HPQZDKNiNyJxVO8Zs0aHDhwAEqlMmkjRTIyMrB+/XocPnwYHo9n1g9LiBWlUokNGzbg2LFj2LNnDyoqKmA0GoU2Ky5hw7V/97vf4fLLL8f3v/99PPjgg/Q6ugCoaAbQ39+P//7v/0ZPTw90Oh2WLl2KnTt34uKLLwYA/OIXv4BIJML1118Pr9eLSy+9FM8+++y027V//35ce+21uOmmm/Dzn/88KcIpJ4vX6+XCjjdv3kx7H8MYGhpCZWUlCgsLUVpamrQPzFAohNraWm6MVCz1EJ5xNRpsg5IV0WxDkp3rk21QAoBcLodCoZhwOdVRAIFAAMeOHcPChQtnRUh+LHR3dyMrK0toMyhTSFpaGjweD2w2W0J4Xy+UefPmoaurC21tbSgoKJjS72bnKPZ6vdwzK3w7fH8wGIRYLB7VGcgK4nChPBXvF4PBgJUrV6KmpgZyuTxpRZHBYMDGjRtx8OBBBAIBLFu2LGnf3+GIxWIsX74cOp0OlZWVWLZsGXUoRYFhGHz1q1/FqlWrcNVVV+HkyZP47W9/mzTtgKmGZs+OU/785z/ja1/7Gv7v//4Pd999t9DmxDV2ux2HDh2CXq/HihUr6DiXMCwWCw4dOoTS0tKkDvEihKChoQFmsxkbN26c8bkw2eEe4Z6ayMYpuySEcJ5ruVw+oshkMigUCshkMm5fLKHhJ0+e5H57MjS6AoEA3n//fWzevDkpstAmQ/ZslurqaqSkpGD+/PlCmzIj9PX1oba2FhdddNGEDd1gMAifz8eJ34kKcD4nBNtZN1YHHluEuK5aW1tx8uRJbNy4cUam8YxX3G43Dh48CJ1OhxUrViRtpNhY9Pf3o7q6GkVFRZg/f35SvN8mS3d3N66++moolUq8+eabNNHcJKCiOc4IBoN45JFH8MILL+Bvf/vbiPmhKaMZGBhAdXU1CgoKsGDBAvrADKO/vx9VVVVYvHjxlHspEo2zZ8/i3Llz2LRpU1xnUo4MhZyoARwKhcAwDGQyGSekI9eDwSBOnz6NsrIypKamQiaTQSwWz+p7paenBydOnMBFF100q38nSzKJ5o6ODpw7dw5btmwR2pRpJRQKwe/3w+fz4ejRowCAOXPmwOfzcc+F8KXP50MgEACAEZ1q7HMgsgOOFcbx3sl88uRJdHZ2YtOmTUk9T6/X60VlZSWUSiVWrlwZ9/+3mcRms+Hw4cPUcRIDLpcLt912G6qqqvDOO+/MqkSDMwEVzXGEw+HAzTffjFOnTmHHjh2YN2+e0CbFNa2trTh+/DiWLl2KvLw8oc2JK7q7u1FXV4fly5cjNzdXaHMEpaenB7W1tdiwYcOIDPiJDiEEgUBgVCM6skFtsVg44ej3+wGcT6oklUo5gS2TybhtqVQ6aj18OxFEaH19PaRSadI0CJJJNPt8PnzwwQcJMfc2IQTBYBB+v58TwOx65DZ770YKYLFYDIlEAq/XC51OB41GM6qDLHJ7NnkiCSGora2F2+3GunXrkloQ+f1+VFZWQiwWY/Xq1UmRzDNW2CF6hBBUVFQkdQfLRBBC8Pjjj+MXv/gFXn31VZpcmAdUNMcJ7e3tuPLKK5GWloY33nhj1mcHvRAIITh58iTa2tpQUVEBk8kktElxRUdHB44ePYry8vKkH9M5PDyM/fv3o6ysLCkTynR3d6OhoQEXXXQRpFIp58lmG+aRjfXxGvfsq0IikXBCOto6u82WyO3pbPgSQvDBBx8kVYKYZBLNAHDgwAFkZWWhqKho2v4G2ynFFr/fP+Z2+H0y1nYoFAIw8r4Zq3MqsvOKLey90tjYiK6uLmzZsiUhOq6mkmAwiP379yMlJQVlZWVJ9/vDCQQCOHz4MILBINasWTPjQ43imWAwiCNHjsBisWDNmjVJMTTnQnjttddwxx134Mknn8Q3vvGNpL6vYoV2U8UBlZWVuOaaa3D99dfjl7/8ZVI0eiZLIBBAbW0t7HY7Nm3aNG6CpmSkra0Nx48fx+rVq5N+vIrH48Hhw4dRUlKSlII5GAzi+PHjWLBgAfdMCQ/l5kOkx2w8oeByuUYIjPB1VniLRKJRInqs9fBtsVg8aj1yWyQSYXBwEABox+MsJjMzE729vSgqKuLEbTAY5Eq07fBl5Hr4tt/vRzAY5P5e+DUZ3gHECl+lUomUlJSoHUhSqfSCvb/FxcVob2+flqRg8Q7rWd27dy+ampowd+5coU0SDIlEgjVr1qCmpgYHDx7EunXrqHD+FDbJZ2NjI/bt24dVq1bR+YnH4aabbkJRURGuueYanDx5Es888wy9liaAepoF5q9//Su+8pWv4Cc/+Qnuvfdeoc2Ja3w+Hw4fPgyGYVBRUUFv7gjCBXOye9+DwSAOHDgAtVodc6bs2caZM2fQ29uLTZs2xcXvJ4QgFAqNEtKRwiWamIkmiMLFDXC+Y4DtHGCFdLjIZrdFIhFXwrcjj7GFYZio2+w6+7ejrU/X/2E6Pc1sEyEUCoEQwv0fI9fHWoaXsfYFg8Go2+x6+DK8+Hw+MAyD8CYMwzBjdqZEdr5M1EETKY7j4f4Bzk+jVl9fj4suuigp339DQ0PYv38/jaLC+fuxpqYGLpeLCucx6OzsxJEjR2hm7Rjo7OzkprZ98803kyZCazJQ0SwQoVAI3/3ud/Gb3/wGv/zlL3HddddBq9XGzcs53nC73aisrIRaraZJMMagvb0dDQ0NWLNmTdILZkII6urq4HA4sGHDhqS8VlwuFz7++OOkmK+c9YKz5eDBgygoKEBqaiq3L1J0RRNvYx0bT/SNJR4nIlJAh2+H7xtvORZOpzPqnNSsXeMtoxU+v4cVreN1METrrIjcF9mREdnxUVNTg+LiYmRlZXGCdzaN5R0LQggOHToEtVqNpUuXCm2OIHR3d6O+vh4bNmxIimnHxiMUCqG6upob702F80jYzNrz589HcXGx0ObELX6/H21tbbj77rtx7tw5vPvuu1iwYIHQZsUlVDQLgNPpxJe+9CUcPXoUv/3tb6HVajEwMACJRIL09HSkp6cjLS2NzqP2KXa7HZWVlUhPT8fSpUtnfcOIL1Qwj+TcuXNoamrC5s2bkzYZSE1NDUQiEVasWCG0KTOKy+XCRx99hMsuu0ywYS7RPLHjidJoQjWayI0kEAigqqoKFRUVo5IDjSe8own3aEJ+LK+6UB29J06cgM/nQ1lZmSB/Xyjsdjv27NmTNNOpjcWZM2fQ3t6OzZs3J71QZIWzx+PBunXr6PC+CKxWKw4dOoT8/Hw6w8qnEEIwNDSE/v5+9Pf3w2q1QqPRwGQy4Y9//CP++Mc/4vXXX8ell14qtKlxBxXNM4zFYsFll10GtVqNv//971wYRCgUwuDgIHcRDw8PQ6/XcyLaYDAkpVgcHBzE4cOHUVBQQOfgGwM26Rcdw3wes9mMQ4cOJYWHNRpWqxUHDhzARRddBKVSKbQ5M0pLSwu6u7uxfv16oU2ZUZItERhw/l6vqanBpZdemnTvhYaGBrhcLqxZs0ZoUwSBEIKqqiqEQiGsWbMm6f7/kVDhPD4OhwMHDx5EWloali1blpRtabfbjYGBAfT392NgYACEEKSlpXEaI7yt8Morr+Cuu+7CM888g1tuuUVAq+MPmghsBunu7sYll1yC+fPn4+WXXx7hSRaJRDCZTDCZTFi4cCE8Hg93gVdXVyMQCMBoNMJkMiEtLQ06nW7WvyjYeYYXLlw4rVlSExUqmEfidrtRU1ODRYsWJa1gJoTgxIkTKC4uTjrBDAB9fX008UuSwIbfsx3MyURpaSk++ugjDAwMJOWzn2EYrFixAnv37sXp06eTPpRUJBJh5cqVqK6u5pKDUeH8HzQaDTZu3IjKykpUV1cnxRA/r9cLs9kMs9mMgYEBuFwuzhFXVFQEvV4ftfPgC1/4ArKysnD11VdjeHgY999//wxbH79Q0TxDNDc3Y9u2bdi8eTNeeOGFCefXUygUmDNnDubMmQNCCOx2OwYGBmA2m9HY2AiGYWA0GpGWlgaTyYSUlJRZJaLZJA50nuGx6enpwdGjR1FRUZGUjaZI2J72jIyMpMssG05vby8cDgdWr14ttCkzTjAYhNlsxsKFC4U2hTIDiEQipKeno6+vL+lEs1wux7x583DixAls3rx5Vr37Y0UqlaKiogKffPIJDAYDMjMzhTZJUMRiMVatWoWqqiocPnwYa9asofM4h6FUKrFhwwYcPnwYBw8exOrVq2dVaL/f74fFYuFEss1mg1arhclkwuLFi2E0Gnl1pGzdupUb6jQ0NITvfe97SfmciYTeUTPAiRMncPHFF+PGG2/Ez372M96hIQzDQKvVQqvVori4mBuPYDab0dvbi5MnT0IikcBoNHIlkZOKtbW14dixY6ioqKBeozEYGBhAbW0tysvLaf18yrFjxxAKhbB06dKEve4vlFAohBMnTqC0tDQpvQwWiwUymQwpKSlCm0KZIdLT09He3o7S0lKhTZlxioqK0NLSgs7OzqTNDqzValFWVoba2lps3rw56aegZIXzoUOHUFNTg4qKiqQMRY6GTCbDunXrRnjkE1U4+3w+DA4OckJ5eHgYKpUKaWlpmDdvHkwm0wXnRaqoqMDevXtxySWXYGhoCD/72c+Stn3FQkXzNFNdXY3t27fj61//+pT11DAMA4PBAIPBgLlz5yIUCsFqtY4Q0SKRCKmpqZyIHi8UI55oaWnByZMnaVKrKFitVlRVVWHp0qVJP+UGS0dHB7q6urBly5ZZH3I1Hm1tbWAYBvn5+UKbIgj9/f1IT09P+pd6MpGeno6jR4/C7/cnXUeRWCzGggULcOrUKWRnZyftsy8nJwdWqxXV1dXYtGlT0tYDi0QiwerVq3HgwAHU1dWhvLycPhPDEIvFqKioQE1NDQ4cOIB169YlRNJdt9vNiWSLxQKbzQaNRgOj0YiioiKYTKZpGZK1ePFi7N+/H9u2bcPQ0BCef/75pI5goInAppHdu3fj6quvxpNPPomvf/3rM/Z3Q6EQhoeHuZtrcHAQwWAQBoMBRqMRqampMBgMcdfIaG5uxqlTp7B27dqkHZM6HjabDfv370dpaSmdPuFT7HY79u7di1WrViEjI0NocwQjEAjgo48+wrJly5K2M+Xjjz9GaWkpcnJyhDZlxknGRGAsu3btwoIFC5CdnS20KTMOIQR79uxBXl5eUr8TQqEQDhw4gJSUFCxfvlxoc+ICr9eLffv2IS0tLakjsKIRCoVQW1sLu92OdevWxdVMG4QQOBwODA4OckLZ6XRCp9NxjrDU1NQZtbm7uxsXX3wxFixYMConUzKRvN0F08w777yDz3/+8/jNb36DW2+9dUb/tkgk4jzRJSUl3JhoVkR3dHTA5XJBq9VyAjo1NRVqtVqwB2tTUxMaGxupYI6C0+lEZWUlioqKkrpxFE4wGERNTQ0KCwuTWjAD5yM0lEpl0o7rc7vdcDgcdHx/EpKeno7+/v6kFM0Mw2DBggWor69Hfn5+0nqARCIRysvLsWfPHqSlpSVlx1kkcrkc69atw759+yCTyZI+WVok7DVTX1+PAwcOYP369YIJ50AgAKvVisHBQW4ZDAah1+thNBqxZMkSpKamCtohmp2djU8++QTbt2/HVVddhX/84x9Qq9WC2SMUyfmEnWZefvll3HXXXXjppZdw3XXXCW3OiDHRhYWFAACPx8P1YrW1teHo0aOQSCScgE5NTYVOp5uRm/Ts2bM4e/Ys1q1bl3QJXWLB4/GgsrISWVlZSTl2LxrHjx/nQhSTGb/fj7Nnz2LlypVJ603o7++HXq9P2PFplMnDhmgTQpLy+s/IyIBGo8G5c+eS+v2gUqlQVlaGuro66PX6pGzQR6JSqbBu3Trs378fUqkUJSUlQpsUV4hEIqxYsQL19fXYv38/1q9fP+2zThBC4HQ6R4jk4eFhKJVKpKamIi0tDaWlpdDpdHE3pNJoNGLXrl246qqrcMkll+Ddd99NujY7Fc1TzLPPPotvf/vb+Oc//4lLLrlEaHOiolAokJ2dzfXOs1N3sDdyS0sLPB4PUlJSoNfrYTAYoNfrp/xGPnPmDJqbm7F+/XrodLop+97ZQiAQwOHDh6HX67FkyZKkbBSORXd3NzeOOd5eLDNNU1MTdDpdUntZzWZzUv/+ZMZoNMLr9cLpdCZlIijW23z48GEUFhYmdcdRVlYW8vLyUF1djY0bNyb9+GYASElJwZo1a3DgwAEolUrqhY+AYRiUlZXh6NGjnHBWqVRT9v0ejwdWqxVDQ0PcMhgMQqfTITU1FXPnzkVqamrCTBGp1Wrx/vvv44YbbsCWLVuwc+fOpIr0o6J5Cnnqqafwv//7v9i5cyfWrVsntDm8EIvFnIeZDf91u90YGhrC0NAQenp6cOrUKQSDQWi1Wk5EGwwGaDSaSYm5cMGs1Wqn+iclPIQQ1NbWQiwWo6ysjArmT3E6naivr0dZWdmUvtwSEa/Xi3PnzmHdunVJe30QQjAwMICVK1cKbQpFANgIKbPZnJSiGQBMJhMMBgOampqSfsq1hQsXYt++fTh58iSWLFkitDlxgcFgwMqVK1FTUwOFQgGj0Si0SXEFwzBYtmwZGhoacODAAWzYsGFSItbv948Qx1ardYTzKSsrCwsWLIhLLzIflEol/vGPf+DWW2/Fxo0b8dFHHyEvL09os2YEKpqniCeffBK/+c1vsGfPHixbtkxoc6YEpVIJpVLJJRZiw0rYhwEb1s0wDHQ6HXQ6HeeNTklJGfehcO7cOZw7dw4bNmyggnkMCCE4duwYHA4H7TEPgxCCuro65ObmJuUYxkiamppgMpmSOg+A3W5HIBCAwWAQ2hSKQKSlpWFgYCCp52hfsGABDhw4gOLi4qRN0gOcdwCsXLkSe/fuRUZGBp2W8VMyMzOxaNEiVFVVYePGjUnbwRQNhmGwdOlSHD16FAcPHsSGDRvGvY+8Xi+Gh4cxNDTELV0uF5RKJZdTqKioaMaGOc40UqkUL730Eu69915s2rQJ+/btS4qp76hongKefvppPP3009i7dy8WL14stDnTBsMw0Gg00Gg0yM3NBXA+A6HD4eAeHO3t7RgaGgIhBFqtlhPROp0OWq0WYrEYbW1tOH36NNatW0cFcxSam5vR3d2NjRs3JnW4XSRnz56F1+vFokWLhDZFcLxeL1paWhIuqmWqMZvNSE1NpR1LSUxaWhqam5uTdlwzAG52jHPnziW9t1mj0WDx4sWoq6vDZz7zGfoO/ZTCwkK4XC4cOnQIGzduTOrOlbFgPc61tbU4ePAg1q9fD6lUCo/HM0ogezweqNVqzlmUn58PnU6XVHUqEonwzDPP4Ctf+Qq2bduGffv2zfpOKiqaL5AXX3wRjz32GD766KNZLZijIRKJuCRjLOEe6eHhYXR1deHkyZPw+/1QKBTwer3Iy8uDz+eD2+2GQqFI2obOWHR3d+PUqVNYv349TWYSxtDQEBobG7F+/fqkzRIbTnNzMzf1RDIzMDBAxzMnOXq9HsFgEDabLalzY5SWlqKyshIlJSVJLxTz8vLQ29uLo0ePJnWSxEgWLlwIl8uFw4cPY/369bSzMYxgMAi73Q6TyQSr1YoPP/wQDMPA7/dDo9FAr9fDZDKhuLh41nqQ+cIwDJ577jl86UtfwiWXXILdu3fP6qgv2vK8AF5//XV8/etfx7vvvotVq1YJbU7cMJZHmhCC9vZ2NDQ0IDc3F36/H8eOHYPT6YRMJoNWq+W80VqtFikpKUn5MLdarairq0N5efmsfvDwJRgMoq6uDiUlJbReAPh8PjQ3N2PNmjVCmyIohBBYLBbMmzdPaFMoAiISiWAymWA2m5NaNKempkKv16O5uRnz588X2hxBYb2Gu3fvRmdnZ1KEjsYCwzBYsWIFDh48iLq6uqTsUCCEwOPxwGazwWazYXh4GDabDQ6HA2KxGDqdDunp6TCbzRCLxVi3bl3Sd0KNh1gsxp///Gdcf/31uPzyy/Hhhx/O2vB/Kponybvvvosvf/nL+Pvf/47NmzcLbU7cYzabcezYMZSXl48YixoIBGC327mHVltbG+x2O/x+P9RqNVJSUkYIaY1Gk9AJFMbD4/GgqqoK8+fP58aRU85z6tQpiMViKo4+pbm5mZvDMZmx2WwghCS1UKKcx2g0wmw2J/089qWlpTh8+DCKi4uT3hOmUCiwfPly1NXVwWg0Jn3iSBaxWIyKigp88sknaGxsnNVTlXm9XthsNtjt9hHLQCDAhVdrtVrk5uZCq9VCqVRynQiBQAAHDx5EfX09Vq1aNWvbnlOBVCrF3/72N3z2s5/F1VdfjXfffVewea+nEyqaJ8Hu3btx44034k9/+hMuu+wyoc2Je6xWK6qqqrB06dJRyZvYzKfh3kO2FzD8Idff3w+73Y5QKASNRsOJaFZIJ7qYDgaDqKqq4kJ/KP/BbDajtbWVTi/1KX6/H83NzaioqBDaFMFhxzPT64JiNBpx9uzZpB7XDJzPpK3T6dDc3DyrxVCsZGVlITs7G/X19Uk9y0AkcrkcFRUV2LdvH7RabUJ31BNC4PV64XA4YLfbuWKz2eDz+aBSqTgHTEFBAbRaLTQazYTRjBKJBGvXrsWBAwe4CEB6/URHoVDgX//6Fy6++GLccMMNePPNN2ddxx0VzTw5fPgwrr76avz617/G5z73OaHNiXucTicOHTqE+fPnx5ySnmEYLnN3eFIBQghcLtcIMd3T0wOHw4FQKMQ9GFkhzS7j/aYlhKChoQGEECxfvpw+lMMIBAI4cuQIFixYMGvDffjS1tYGjUaT9F5mALBYLLQeKADOj2sOhUKw2+1Jn2By7ty53HCWZBzmFMnixYuxe/dutLe3Iz8/X2hz4gadToeysjLU1dVh48aNcX/fEELgdrtHCGNWKPv9fiiVSq4NmJuby61fSBtQKpVi7dq12LdvH06cOJGUuYv4oNFo8N5772Hr1q245ZZb8NJLL82qZxAVzTw4duwYLrvsMvzgBz/AbbfdJrQ5cY/X60VlZSXmzJkzJd5ThmGgVquhVquRmZnJ7WcfpOG9jG1tbXA4HPD5fFAoFJw3OryoVKq4EKgtLS3o6+vD5s2bZ9XDZSo4ffo0ZDIZioqKhDYlLgiFQjh37hyWLFkSF9eukLDjmUtKSoQ2hRIHiEQipKamwmw2x33jf7pJT0+HQqFAe3s7CgsLhTZHcKRSKZYtW4aamhqkp6dPag7e2UpOTg5sNhuqqqqwadOmuBi76/f74XA4RhWn04lQKMQN3dNoNDCZTNz6dCUIlcvlWLNmDfbt2weVSkXbIxNgMBjw73//G5s2bcLXvvY1/Pa3v5017RUqmmPk7NmzuPjii/Gtb30L999/v9DmxD1suLFWq5326YEYhoFKpYJKpRqV7t7r9cJut8PpdMLhcHChvk6nk/tcpJhWq9WQy+UzcpMPDAzg5MmTWLduHX2RR2C1WtHa2orNmzfPmgfuhdLZ2QmxWJzQoXRThd1uRzAYhF6vF9oUSpxgNBphsViSvlHLMAxKSkpw5swZFBQU0OcngIyMDGRmZqKhoQEVFRW0TsKYP38+bDYbampqsGbNmhkZ7hIMBuF0Orm2Wbgw9nq9kMlkXJtMr9cjNzcXarVasKF4Go0Gq1evxsGDB6FUKuk7eALS09Px4YcfYuPGjXjooYfw05/+dFbcc1Q0x0B7ezu2bduGW265BY888ojQ5sQ9hBDU1dUBgOBjQORyOeRyOUwm04j9oVAILpeLe0g7HA50dnbC4XDA4/FALBZzXu3wotFopmyKLJfLhZqaGixZsiTppw2KJBQKob6+HnPnzkVKSorQ5sQFhBA0NTVh7ty5s+Llc6EMDg7CYDDQ8cwUDqPRiNbW1qQf1wyc9yCeOnUK3d3dyMnJEdqcuGDJkiXYtWsXurq6uJk9KP/JqP3JJ5/g1KlTU+boCAQCnDAOL5HtLNZZYTQaOaEcDx7vSFJTU7FixQrU1tZi/fr1dCaPCZgzZw4++ugjbNy4ETqdDt/73veENumCoaJ5AgYHB3HxxRfj8ssvx49//OOkfxHHwokTJzA8PIyNGzfGbbixSCTiHs6RhPeAsqW7uxtOpxMulwsikQhqtRoqlYpbhq/HEiIUCoVQU1ODrKwsOsZqDBobG8EwDObOnSu0KXFDX18ffD4fbex9yuDgIO1sooxAr9fD6/XC7XYnfaZkkUiE4uJiNDU1ITs7m7ZdAMhkMixduhQNDQ1IS0uDXC4X2qS4QSqVchm1jUbjiCFw0WCTtrpcLq59FL70er2QSCQjHA8Gg2HGI/qmkuzsbLjdbhw6dAibNm2CWq0W2qS4pqSkBB9++CE2b96M1NRU3HPPPUKbdEFQ0TwOfr8fn/vc57Bo0SI888wzCXdzC0FzczM6OjqwcePGhH0hicVibpqrSILB4IgXg8vlgtls5vYFg0HI5fJRgpotSqUSIpEIJ0+eRDAYxJIlSwT4hfGN3W5HU1MTNmzYQL2IYZw7dw6FhYVx2xE101gsFixdulRoMyhxhEQigU6ng8ViSXrRDAD5+fk4ffo0BgcHacK8T8nOzkZnZydOnDiBFStWCG1OXJGSkoJly5ahrq4OW7ZsgVKphN/v59o64YVtA4VCISiVSq6No9FokJ6ezolkmUw269rOxcXFcLlcqKysjJtx4PHM4sWLsWPHDlx88cWYN28eLr74YqFNmjRUNI/DN77xDVgsFvzrX/+ijfcY6Ovr48bnztZMx2KxmMvIGAkhBD6fb1SP6+DgINxuN1wuFwghkMlk8Pl8yMzMRGNj44gXjlKpTGpRxGYSz8vLo2NVw7DZbBgcHMTKlSuFNiUuYL0b1NNMicRoNGJwcBBz5swR2hTBkUgkyM/PR3NzMxXNn8IwDJYsWYKPP/4Y+fn5SV0vbJuFFcJsO0UikWDXrl1gGAbBYBAymWxEOyUjI4NzDCRrm2Xx4sVwu92oqqrCunXrqEaYgLVr1+K5557DDTfcgMOHD2PevHlCmzQpqGiOwrPPPou///3vqKqqmrUCcCpxOByora3F8uXLk7YhyzAMN4Z6rDoghGBwcBCVlZUoLCyEXC6H2+2G1WrlXlihUGjEC4qdeostKpUqIUOaYqW7uxt2u53OQRxBc3MzcnNzEzZ6Y6oZHByEVquN++nkKDNPamoqzpw5I7QZcUNhYSE+/vhjuFwu6n3/FJVKhdLSUjQ0NGDz5s2zVvAEAgG43W6usO2M8MK2OcIj4oqLi9Hc3IzU1FQsXbqUPmfHgB0Hvm/fPhw/fpxGPcXAl770JZw4cQJXXnklDh06lJBjwqloHoOPP/4YDz30ED788EM63jQG/H4/Dh8+jPz8fDrechwIITh+/DjmzJkz5gOWEMKNxwsvVqsVXV1dcLvd8Hq93DzWCoWCE9PsOruUy+UJ1xDw+/04duwYFi1aRF/SYXi9XnR0dGDTpk1CmxI3WK3WpO2co4yPwWCAzWaD3++nzxEAarUa6enpaGlpmfaZLBKJ4uJitLe3o6WlZUqmxJxp/H4/3G43PB7PmEu32w2/3w+xWDyq491oNI7olB/LU5yRkYG9e/ciOzubZoqOgkQiwerVq7F3717odDqqF2Lghz/8IU6ePImbbroJ77777rRNEzZdJJa1M0BTUxP+67/+C8888wzWrVsntDlxD5spW6lUYsGCBUKbE9ecPHkShBAsXrx4zOMMw0ChUEChUETtgQsGgyNeiuy62Wzm1r1eLwCMENLRilQqjRuv9ZkzZ5CSkkI7XiJoa2tDamoqdDqd0KbEDVarlYbfUsaEFQJDQ0NIS0sT2py4oLi4GFVVVSgtLU24Rup0IRKJsHTpUlRVVSE7OztupnwMhULweDxRC/veDwQCnCAOf9frdDpuXaVSTfodr9FosHz5ctTX10On09EohSioVCqsXLkShw8fRkpKCu3MnQCxWIyXX34Z69atw4MPPoinn35aaJN4QZ+eYQwPD+PKK6/E7bffjltvvVVocxKCxsZG2Gy2WR3iNBX09/ejtbUVW7ZsuaDxP+FTYUUj/KXLvmA9Hg/sdjsGBga4fcFgEGKxeISIlsvlYy6nO5mHzWajczKPASEEra2tUTtakpFQKIShoSEsW7ZMaFMocYrBYIDVaqWi+VOMRiOUSiW6urqoNyyMtLQ0ZGZm4sSJE9OeLyIQCMDr9cLr9XLvZHadXbrdbvh8PgDg3r2sAGanZAqPMJNIJNP6vszJycHAwADq6+uxbt06+m6OQlpaGhYsWIDq6mps3rwZCoVCaJPimpSUFLz99tuoqKjAokWLcOeddwptUsxQ0fwpwWAQN910E4qKivDjH/9YaHMSgp6eHpw9e5ZmD5wAn8+H+vp6LF68eEbGx4tEIm5s0nj4/f5RvdherxfDw8MjXuyBQGDEeG1WTEcrfAU2G7ZeUFBA52SOoK+vD4SQmKb/SBZsNhsYhqG5JihRMRgMsFgsQpsRNzAMg4KCArS2tlLRHMGiRYvw0UcfwWKx8E4KFgwGOSE8Vgl/r4a/R8M7pBUKBfR6/ajO63hxQixevBh79uzBuXPnUFJSIrQ5cUtRURGGhoZQVVWF9evXJ2VyND4UFhbizTffxGWXXYZ58+Zh8+bNQpsUE1Q0f8r/+3//D+3t7aisrKQXewzY7XbU1dVhxYoVY07NRPkPDQ0NcTneRSqVQiqVTihU2YbBWL3jdrt9RCMhGAyCYRjIZDLOQx0pqCOXZrMZw8PDWLVq1Qz98sSBbeTGSwMqHrBarTAYDNTrQYmKwWBAU1MTCCH0OvmU3NxcnDhxAkNDQ3RmgjAUCgXmzp2L48ePY8OGDfD7/fD5fPB6vdwycp0tgUAAAMbsPNbpdMjIyBghkhNx+iWJRIIVK1bg4MGDSEtLo8OEosAwDJYvX459+/bh2LFjWL58udAmxT2bNm3CL3/5S1x//fWorq5GYWGh0CZNCBXNAF588UX86U9/QlVVFRWAMRAMBlFdXY2CggJkZ2cLbU5c09nZiYGBAWzdujXhXpYsYrE4Js81MDIELbz4fD7Ogx3eICGEADgv4A8cOMA1LMYqcrkcUqkUMpksKTq2XC4X+vv7aRhyBENDQwmZdZMyc+h0Ovh8Png8nrgZqyo0UqkUubm5aG1tTYoGPSGEex/5fL6ohX1HOZ1O7NixA8B5oThWJ69Go7ngyKpEJDU1FcXFxairq8OmTZuS4v07GcRiMVavXo09e/bAaDTSvBsxcMcdd+D48eO48sorcfDgwbjXYEkvmvfv34/7778fO3bsSMgMikJw/PhxSCQSmvhrAlwuF44ePYoVK1YkzRgXiUQCiUQy7phrFkIIzp49i7a2Nixfvpzr4WeL0+mE1Wod0ehhe/YlEglkMhknoiPXxzomlUoTymPb1taGjIwM2uiPYGhoCPPnzxfaDEocI5FIkJKSAqvVSu+fMAoKCrB///6EmqGAEIJgMDji/RBtPXKbjTQI73gNX09JSeHWrVYrWlpasHXrVjq13xiUlpair68Pp06dojk2xkGpVKKsrAy1tbUwGAx0GFEM/N///R8++9nP4uabb8Zbb70V150ySS2azWYzbrjhBvz0pz/F1q1bhTYnIejq6kJXVxe2bNmSUAJkpiGEoL6+nk7XMA5+vx9NTU0oLy+POWFPKBQa5SkIbySxIeOR+0OhEIDzjWlWQIeL6fD1aEUkEs2YRyEUCqG9vT0pPEJ8CAQCsNlsNLyUMiF6vR5DQ0M0GioMvV6PlJQUdHZ2zmgoJCEEfr+fK4FAgHs+h+8fa19kRNJYHaJKpRI6nW5Ux6lMJos5WVZaWhp6e3vR2tqK0tLS6a6ShEMkEqG8vBx79+5FZmYmTCaT0CbFLZmZmcjLy0NNTQ02btwY1yIwHpBIJHj99dexatUq/PSnP8V3vvMdoU2KStKKZkII7rjjDqxduxZ333230OYkBE6nE0eOHEFZWRmdfmAC2tvb4XA4UFFRIbQpccvZs2eh1+uRkZER82dEIhGXLIUPbCNtvIaZx+Ph1sPPDwaDAM6PWRpLTLNCfKxl5D6xWBxTA25gYAAAkJ6ezut3znZsNhs3RpBCGQ+9Xo+enh6hzYg78vLy0NHREbNoDoVC3PMw1uVY68D5Z2h4x2VkpyU7DWK0Ds3p7LRkGAZLlizBwYMHkZ+fT58xY5CSkoL58+fjyJEj2Lp1KxWD47Bw4ULs27cPJ0+exJIlS4Q2J+7R6/V49dVXsXnzZlx00UVxm+MmaUXz7373O9TU1KChoWHWj0eZCkKhEGpqapCbm0t77ifA4/HgxIkTWLFiRcKEwM00brcbLS0t2LBhw4z8PVbATiZUMxQKjdkQjNzndrths9nGbEiywjvclkhRHV56e3uRkpKCjo4Obp9YLB5xjlgsjlmEzxasViv0en1S/WbK5NDr9Th16lTSJQMLhUIIBoMIBAIjCrsvGAxiaGgIDQ0NEIlEo84Lf24FAgEuSidc8EbrIGSnQYoWrRPvz6vU1FSkpaXh7NmzVOhEobi4GF1dXThz5gwWLlwotDlxi1gsxsqVK7F3716YTCYacRgDK1euxKOPPoovfOELqK+vj8vQ9qQUzSdPnsSDDz6Id955h05EHiOnTp1CKBSiY1lioKGhAenp6XSaoHFobGxERkZGQoTZikQiLunLZGG9NeM1TtnCzqktl8vR0dExqtEbCAS4cEUAIwQ1K6Qj9411LNYST43c4eFhmr2VEhNarRaBQAButzuuIqNYURte2Ps7WokUvePtY0UugDE72tgkV2azGWlpaZDL5VCr1aM67iIFcbw9C6aL+fPn45NPPkFxcXFcXTfxQniW6JycHPo8HgeNRoNly5ahvr4eer2e5leIgYceegg7d+7E/fffjz/84Q9CmzOKpBPNXq8XX/jCF3DffffRccwx0t/fj5aWFmzevJmG40xAd3c3zGYzLrroIqFNiVscDgfa29uxZcsWoU2ZMUQiETfGbiKam5vhdDqxfv36qOeMJcInakyzU4JN1EAP94qztovFYm4ZuR65TyQSjfpMtGORZaz9DMNwjfXh4WHaGUWJCbFYjJSUFAwPD3PihxCCUCg0qgSDwQn3s+uRy0gRPN52KBQa0eHFMEzMnVms2I3WMTaWQI4mcvv7+1FXV4dFixbR3CQR6HQ6ZGdn48yZMygrKxPanLhEp9OhuLgY9fX12LRpE72GxiE3NxcDAwOora3F+vXrk6Lj6UIQiUT4y1/+gmXLluFvf/sbbrjhBqFNGkHSieaHH34YUqkUjz/+uNCmJAR+vx9HjhzBokWLJpzPN9nx+/1oaGjA4sWLafbNcThz5gxyc3Pp9RSF9vb2Cccb8hHhfGGFxVhiOhaBwAp6NgHbeGJjLJES7feyoaRHjx7F8ePHRwnrsYR25PHw/ZH7xtqOth/AmOeNdU74uRPtYwnfHquRFbnP7/cDOD80JLwOwwXaRNvs+ljL8dbH2zdeYQVktP18lmyJ3Pb5fKipqQHDMKMEa3hdRuuwmajDRywWc2HHE3UsRetoEoK0tDQwDIP+/n7aCTUGpaWl2L17N0pKSuh7Kgrz5s1Dd3c3mpubUVJSIrQ5cc2SJUuwe/duNDc301l6YiA3Nxe///3v8eUvfxmrV69Gfn6+0CZxJJVo/uCDD/DCCy+grq5uWhqbs5Hjx49Do9GgoKBAaFPinpMnT0Kr1dK5+cbBZrOhu7ubeuKjYLfbYbfbBc0bEO79EoJognp4eBj19fVYvXr1hGJpLHEVuS8YDE5KzMUqEtnj4edF7os8Fr5/Mnz88ce8zh9LnE8k9MOP8elE4Ns5wQpTNgFUZEdHZGdJZMeJSCRCd3c3hoaGsGzZsjEjG2YyI348wTAMcnNz0dnZSUXzGGg0GuTl5eH06dNxm5BIaMRiMZYvX47KykpkZWXFNM1ksiKRSFBWVoZDhw4hIyMjLsfqxhvXXnst3n//fXzpS1/C7t274ybKNWlEc39/P2699Vb86le/wty5c4U2JyHo6+tDd3c3tm7dmpQNCz4MDQ2ho6OD1tUEnDlzBvn5+XSsWBS6urqQkZGR1AnkWDETicVigV6vT8o8FBOJar/fjw8++ADbt28fde1M5LWezQSDQXR3d1Nv4Rjk5uZi3759CAQCkEiSpikYM/PmzcNHH30Em80GrVYrtDlxidFoRE5ODo4fP47Vq1cLbU5cYzKZkJ+fj/r6emzYsCHpnsWT4Re/+AXKy8vx1FNP4bvf/a7Q5gAAkmIgAiEEt912GzZt2oRbb71VaHMSgvCwbCpwxocQgoaGBhQXF9Pe1nGw2+3o7e2loVxRIISgs7MTubm5QpsSlyRzErBIb+xYBUBUT2q4RziZ0Ol0cLvd8Pl8QpsSd2i1WqhUKvT29gptSlyiVCqRl5eHs2fPCm1KXLNw4UKYzWb09/cLbUrcs2DBAni9XjQ3NwttSkKgVqvx6quv4kc/+hEqKyuFNgdAkojmZ555BseOHcPvfve7pGw4TIbjx48jJSUlrsYSxCudnZ1wu900gmECzp49izlz5tBOmCgMDQ3B6/Xymrc6mbDb7dTjQ+GFTCaDQqGAzWYT2pS4IzxEmzI2JSUl6O7uhsPhENqUuEUul2P+/Pk4duzYiMztlNGwYdqnTp2C3W4X2pyEoKysDE888QRuvvnmuHiOz3rRfPLkSXz729/GX//6VxgMBqHNSQjYsOyysjLayTABfr8fJ06cwOLFi2mI2zg4nU50dXXRjoVx6OrqQlZWVtyM3YknCCE0TJIyKbRabVw0tuKRnJwc9Pf3w+v1Cm1KXKJWq5GTk0O9zRNQWFgIhmGoBzUGjEYjF6Z9IfkrkokHHngAxcXFuO+++4Q2ZXaLZkIIvvrVr+Luu+/Gpk2bhDYnIQgEAjhy5AgWL15M55SLgcbGRqSkpAiauCkROHv2LLKzs2n4ehQIIejq6kJOTo7QpsQlXq8XPp+Pjk2l8Ear1VKvThTUajX0ej0N0R6HuXPnorOzEy6XS2hT4haRSIQlS5bgzJkz8Hg8QpsT9yxYsAA+nw8tLS1Cm5IQiEQi/OlPf8I///lP3skup9yWqfyyTz75BFdeeSWys7PBMAzeeuutEcdvvfXWUdkyt2/fPuKcyspKLF++HAUFBXjxxRcvyJ6XXnoJzc3N+P73v39B35NMnDlzBiqVCnl5eUKbEvc4HA40NzdjyZIl1CM/Dm63Gx0dHdTLPA5WqxXBYBBpaWlCmxKX2Gw2qNVqGs1B4Q31NI9PVlYWuru7hTYjbklJSUFWVhaampqENiWuSUtLQ3p6Ok6ePCm0KXGPRCLB0qVLcerUKdrJECM5OTl4/PHH8bWvfU3QHBVTKpqdTieWLVuGZ555Juo527dvR09PD1deffXVEcdvv/12fO9738Mrr7yCp556Ch0dHZOyxWq14sEHH8TTTz9N07vHiM1mQ3NzM5YuXUpFYAycPHkSc+bMoSGjE9DS0oL09HRaT+PQ09ODzMxMweZtjXdsNhv1MlMmBSuaaSjk2GRnZ2NgYICb65sympKSErS3t9OEchOwaNEidHV1YXh4WGhT4p709HSkp6fjxIkTQpuSMNx3332Qy+X42c9+JpgNU9pCu+yyy/CDH/wA1157bdRz5HI5MjMzuRI5ztjpdGLFihVYtmwZDAbDpMOqvvvd72LFihW4/vrrJ/X5ZIMQgmPHjqGgoCBpM9TyYXBwEP39/SgtLRXalLgmEAigtbUVxcXFQpsStxBC0N3djaysLKFNiVtoEjDKZNFoNAgEAtSjEwW1Wg2tVktDtMdBr9dDr9ejra1NaFPiGpVKhYKCApw6dUpoUxKCxYsXo6enB2azWWhTEgKJRIJnn30WP/zhDwW7F2fcrbFnzx6kp6ejtLQUd999NywWy4jjjz76KBYsWACdToc1a9Zg4cKFvP9GTU0N/vjHP+I3v/kN9ZjGSFdXF+x2O+bPny+0KXEPIQSnTp1CUVERHfc9AR0dHVCpVDAajUKbErcMDw/D6/UiPT1daFPiFrvdTiOGKJNCLBZDrVbTcc3jQEO0J6a4uBjNzc00Q/QEzJs3DxaLhQrBGFAqlSgtLUVDQwO9rmJk/fr1uPHGG/H1r39dkL8/o6J5+/bt+Mtf/oJdu3bhJz/5Cfbu3YvLLrsMwWCQO+f222+HxWLBwMAAfv3rX/P+G8FgEHfffTceeughOh9sjPj9fhw/fhyLFy+GVCoV2py4p7+/H8PDw3SM7gQQQnDu3DkUFxfTzqtxYEOzadbssSGEwOFw0PBsyqRJSUmhonkcsrOz0d/fj0AgILQpcQv7jKadC+Mjl8tRUlKCkydP0iERMVBcXAxCCM08zoMf//jH+OSTT7Bjx44Z/9szKppvuukmXHXVVViyZAmuueYa7NixA9XV1dizZ8+I89Rq9aSnh3rhhRcwODiI73znO1NgcXJw5swZpKSk0My9MUAIwcmTJzFv3jzawTABfX19CAQC9LqagL6+PmRmZgptRtzi9Xrh9/upp5kyaTQaDZ1rdxw0Gg2USiX1Do4DwzAoKipCU1MTFYMTUFxcDJfLhZ6eHqFNiXtEIhGWLl2KM2fOwO12C21OQpCWloYf//jHuO+++2Y8q72gWWeKiopgMpmmLCthf38/Hn74YfzmN7+hYbMx4nA40NLSQjNAx0hnZyf8fj8KCwuFNiXuaW5uRmFhIU1uNQ5utxs2m42GZo+D3W6HSqWimbMpk4Z6mseHYRhkZGTQcc0TkJeXB6fTCavVKrQpcY1EIkFpaSlOnTpFOxhiIC0tDRkZGTh9+rTQpiQMd9xxB9LT0/HUU0/N6N8VtDXb2dkJi8UyZQlw/t//+3/YunUrLrvssin5vmTg1KlTNAN0jBBC0NjYiHnz5tFQ2glwOBywWCzIz88X2pS4pq+vD6mpqZDJZEKbErc4HA7qZaZcENTTPDGZmZno7e2lImccJBIJ5syZg9bWVqFNiXvy8/MRDAbR1dUltCkJwYIFC9DZ2Umnx4sRkUiE5557Dj/72c/Q2Ng4c393Kr/M4XDgyJEjOHLkCIDzU80cOXIE7e3tcDgceOihh3Do0CG0trZi165duPrqq1FSUoJLL730gv/2vn378Pe//x1PP/30BX9XsmC1WtHX10czQMdId3c3AoEAncM6Btra2pCZmQmFQiG0KXFNb28vMjIyhDYjrqGimXKhaDQaLsyfMjZGoxHBYJBOFzQBBQUF6OrqotNPTYBIJMLcuXPR2NhIO2JiQK1WIz8/n2Ye58GKFStw++2345577pmxa2xKRXNNTQ3KyspQVlYGAHjggQdQVlaGRx99FGKxGA0NDbjqqqswb9483H777SgvL8e+ffsgl8sv6O8Gg0Hcc889ePTRR6mgiRF2bC7NAB0brJd57ty5NNx4AoLBINrb21FQUCC0KXFNMBjEwMAAFc0TQEUz5UKRyWSQyWTU2zwOIpEI6enpNER7ArRaLfR6PTo6OoQ2Je7Jy8uDz+ejY5tjpLS0FGazedSsQpToPPnkkzh27BjefPPNGfl7UzpIbMuWLeOq/Z07d07ln+N44403MDw8jG984xvT8v2zETYDdEVFhdCmJAS9vb3wer003DgGenp6IJVKYTKZhDYlrrFYLJDL5TQr9AQ4nU6o1WqhzaAkOGyI9mSTjCYD6enpaGtro1NPTkBBQQEaGxtRVFREc8GMg1gs5rzNWVlZtK4mgM08fuLECWzcuJHWVwzo9Xp8//vfx2OPPYZrr7122odOJrzLLBgM4rHHHsPNN9+MpqYmtLa2or+/Hw6Hg857FgWaAZofrJe5pKSEjmWOgdbWVhQUFNAH/gT09/cjPT2d1tM4hEIhOJ1O6mmmXDBqtZp6micgPT0dQ0NDNIx9ArKzs+Hz+ahHMAby8/PhdrvR398vtCkJAc08Pj6EEPh8PlitVnR1deHs2bNYuXIlrFYr3njjjWn/+wmfjvT111+H2+3GF77wBfh8PgwNDcHlcsHlcoEQAqVSCZVKBZVKBbVaza2rVCooFIqkbLDSDND8GBgYgMvlouHGMeB0OjE4OIhVq1YJbUrcMzAwgHnz5gltRlzjdDohEonoEBLKBaPRaGiSnQlQKpVQq9UYGBhAdna20ObELWKxGLm5uWhvb6cRVRMgkUhQXFyMxsZGOhQpBtjM4ydPnkRmZmZSDgf0+/1wu91wuVxwOp2cpmNLIBCATCYboefuvfdePP744/jc5z43rc6thBbNwWAQTzzxBB599FEsXrx4xDFCCDwez4gKdzgc6O/vh8vlgsfjAcMwUCqVnLAeaznbPIuEEJw5cwalpaWz7rdNF01NTSgsLKRT3sRAR0cHMjIyLjhPwWzH4/HAZrMhLS1NaFPiGpfLBZVKlZSdm5SpRa1WU+9NDKSnp1PRHANz5szB/v37sXTpUto2mIDCwkKcPXsWg4ODSE1NFdqcuCc/Px9NTU3o7u5Gbm6u0OZMKayn2OVyccI4fN3tdsPv90MsFo8QxSaTacR2ZJTs3Llz8fzzz+Nvf/sbPv/5z0+b/Ql9p7/22mvwer245ZZbRh0LF8RjEQqFRvyT2OXg4CDcbjfcbjdCoRDkcjn3PWOVRPNWd3d3IxQKYc6cOUKbkhDY7XZYLBasWLFCaFPiHkIIOjo6sGjRIqFNiXsGBgag1+vpVFMTQMczU6YKlUoFl8sltBlxT1paGo4fPy60GXGPTqeDSqVCT08PbU9NgFQqRV5eHpqbm6lojgGRSISSkhI0NjYiJycnoTQG6yX2eDyclooswWAQMplsRCQwK4rZfVKplNfvlslk+J//+R888cQTuOGGG6bNKZiwojkQCOCJJ57Ad7/73UmNyxWJRFCr1VEbZKynOvKfHS6qvV4vGIaBQqEYJaTDl3K5PC5CLAghOHv2LEpKSuLCnkSgubkZOTk5dOqkGBgcHITf76chWDEwMDBAvcwxQEUzZapQq9Xw+Xzw+/00l8c4mEwmzvujUqmENiduYRgGc+bMQUdHBxXNMVBUVISPP/4YbrebDreJgby8PJw5cwa9vb3IysoS2hwQQhAIBEYI4rE0UiAQgFgsHqWLDAbDiCje6YjOuPXWW/GjH/0Ir732Gm6++eYp/34ggUXza6+9hkAggP/+7/+elu+fyFMN/MdbHVnMZjN3MXm9XgDns+JFimmFQjGi8O1Z4Ut/fz88Hg/NAB0jPp8PHR0d2LBhg9CmJAQdHR3IycmhYf8xYLFYsGzZMqHNiHucTifS09OFNoMyC5DJZJBKpXA6ndDr9UKbE7dIJBLo9XqYzWY6hecE5Obm4tSpU1QIxoBarUZ6ejpaWlqwcOFCoc2Je8RiMedtzszMnFZtEAqF4PV6OSEcLorD14PB4AhBzC51Ot0IgTzdWiYaUqkU3/3ud/HEE0/gpptumpa2aEKK5gv1Mk8VE3mrgdEXI7u02+2ciGUvRpFINEpIs2L7QsU1mwG6qKiIipoYaWtrg16vpw2sGAgGg+ju7saaNWuENiXuYYeC0DC1iaHeLspUwoZo02f6+BiNRlgsFiqaJ0CpVMJkMqGzsxNz584V2py4p6ioCDU1NTSnToywU5uZzeZJRaYFg0F4vV5OZ3g8nlHbHo8HPp8PAEboDaVSCY1GA5PJNEIgSySSuA4X/+///m/88Ic/xKuvvoovfvGLU/79CSmaX331VQSDwWmpkKmGzfw6Xi8kG/Yw1kVts9lGiWs2JJy9wOVy+Yj18H1sCITFYoHNZqOiJkZCoRBaWlpGJZijjI3ZbIZEIqFzoMaAxWKBXq+nyWMmgBBCRTNlSqHjmmPDZDLh2LFjQpuREOTk5KC1tZWK5hgwmUyQy+Xo7OykEY8xIJFIUFRUhMbGRk40s4m0wrWC1+sdsc4u2anjIp1vbLh0pGaYDcM2WW/zk08+iZtuumnK21kJ12pjvczf+973Zs24JIZhIJVKIZVKkZKSMu65rLge6yYZHh4ecYwQArFYDLlcDp/PB7lcjhMnTnCCOrIIFVIRj/T394MQgszMTKFNSQi6u7uRnZ1Nr58YMJvNMBqNQpsR9/j9fgSDQSqaKVMGFc2xkZqaykXE0LDj8cnMzMTRo0dpB18MMAyDoqIitLS0UNEcBusRjixslmmz2YyPPvoIgUCAG/IpkUhGiV6tVjtqn0wmmxVimA9f+tKXOG/zl770pSn97oQTza+99hoIIQnhZZ4OJBIJNBoNNBrNuOexvVFerxdDQ0M4cuQI5syZw92cNpttxM3JerBlMhl3o020lMlks1YktbW1IS8vL+keNpMhFAqhp6eHRjHEiMVioREMMeByuSCTyahHnjJlqFQqDAwMCG1G3COVSqHT6WCxWGbdlDdTjVwuh8lkQk9PD4qLi4U2J+7JycnB8ePHMTw8DJ1OJ7Q500IgEODa37EsA4EAAHDt6/Ci1+vh9/tBCMGiRYu4/TS8PTpSqRTf+9738OSTT+Lmm2+e0nZ8wrVGnnnmGXzzm9+kDakJYBiGu7na2tqQnZ2N0tLSqOezPVhj3dROpxODg4Mj9geDQQDgxDNbWI91uLAOL4ngzfZ4POjr66PCJkbMZjPEYjENzY4B9n6i45knxuVyUS8XZUqhnubYSU1NxeDgIBXNMZCdnY329nYqmmNAKpVy9bVkyRKhzZmQYDAIn883bmHbxWyJbB+HO5zYxFljOaOitY2zsrKwZ88eqFQqyOXymfz5CcvNN9+Mhx9+GB999BEuueSSKfvehFKeDQ0NOHr0KD744AOhTUkYAoEA2tvbJ/QCSiQSSCSSmKd3YXvSoj04XC7XqOOhUAjAf7KYRorpaNtSqXRGkw90dnbCaDTSqW5ihIZmx47VaoVGo6HzM8eA2+2m4Y6UKUWpVFLRHCMGgwHnzp0T2oyEICsrCw0NDTScPUby8vJQVVWFhQsXzpjHNBgMwu/3w+/3c23SaOvh26wAjmyjsiUlJQUmk2lMZ9FUtYlSUlKQmpqKtrY2zJs3b0q+c7YjlUrx5S9/Gb/73e+SVzQ///zzuOmmm2ZtSMd00NXVBZVKNeWeLVZkx9qoZZOdjfeQcjgcYz64gJHjvtmH11jLsQofwU0IQVtb27heecp/IISgp6cHq1atEtqUhMBqtVKPfIzQBihlqlEqldx7aLbkRJkuUlNTUVdXx00zQ4mOXC6H0WhET08PioqKhDYn7jEajZDJZOjt7UVOTk7MnwuFQiOEb3ibMnz/WEtW/IrF4jEFsFQqhVqtjnpM6KF6hYWFOH78OObOnUsdFDFyxx13YP78+ejp6Zmyua4TRjQ7nU689NJL+Pe//y20KQkDIQQtLS0oLCwU/CYLF718vEeRYnuspdfrhd1u5x6a4YWFfRBKJJIRAlsikYwQ2Oy0YGq1Gi6Xa8a93InG0NAQCCE03DhGBgcHeTUSkhm32007GChTilQqhVgshsfjoaJ5ApRKJWQyGYaGhmjiwhjIyMhAf38/Fc1RIISMEL1paWk4e/YsAHD72LZetMIKX5FINMqBwhalUgmtVjtif/h6onYAZWZm4tixY+jr66MJamOksLAQW7duxR//+Ec88sgjU/KdCSOa//a3v6GgoAAVFRVCm5IwWK1WOJ3OhB6TNFmxDYwtuMcqTqeTW7fZbGAYBpWVlSNEN+tZjxTa4fsij7P72G2xWDzrxHdfXx/S09MF74VNBAghsFqtdKx8jLjd7inrHaZQAHDTNbrd7glnqkh2GIaBwWDA4OAgFc0xkJGRgVOnTiEQCMy6nDuhUAiBQIBrT0Wux7pkh+iJxWKIxWL4fD6cPXuWmx6VbVexYc7RogcTVfheCCKRCAUFBWhpaaGimQd33XUXvvWtb+E73/nOlLRTE+bOfv7553HXXXfNOtExnbS2tmLOnDmz7gEeK3wFdygUwr///W+sXLkSJpOJE93hvZ/RXgjsnHiRL5VAIMD1jgIYJaTDt/kU9qUjNH19fSgsLBTajITAbrcDALRarcCWJAY0PJsyHSiVSrjdbqHNSAgMBgOGhoaENiMh0Gg0UCgUsFgsyMjIENSW8LZLMBgc0R6JtYS3Y1ixC2BcJwEreKMdYwsrXvbv34/s7GzqnY+R/Px8NDY2wul00pw7MXLllVfinnvumbKEYAmhpmgCMP4EAgF0d3djw4YNQpuSMFgsFgDgetXDRfeFNN7De2mjvZTC97nd7nFfZiwMw4wQ0ZGiOnJ/5D5WeEcKcZFIFFPnlMfjwdDQkOANhERheHgYWq2WdvzFACEEHo+HimbKlENFc+zo9Xq0t7cLbUZCwDAMMjIy0NvbG/M7kRDCidrxlpHCN/KcsdZZRCLRhJ3wcrkcarV6wk79qY6Wy87ORnd3NxXNMaJQKJCeno7Ozk6adydGpjohWEKIZpoAjD+9vb1cantKbExXFmiRSMQllLhQxnrJjtWbHL7NTnMU7UXMrrMwDDNKWIcLbHbd7XZDLpejtbV11HkTlViF+WxieHgYer1eaDMSAq/XCwB0eg3KlKNQKLjrizI+Op2OG76UbGPA2XdtLIV9j/p8PvT19UX9bPj7dqL37nhLhUIRtVM8UuTG89Cp7OxsHD9+nEYV8SA3NxenT5/GvHnzkq4NNVnuuOMOlJaWTklCsLgXzS6XCy+99BJ27twptCkJRWdnJ3Jzc+lNFSOhUAjd3d1xnwU63Ls8lUSK8bFe8JHbg4ODUCgUcDqdMTUswkO8AIwQ0NGEdbRtdn28fWMtGYYR7J4YGhrCnDlzBPnbiQabqCkehiBQZhdyuZwbKkEZH7lcDoVCgeHhYZhMJkFsYBNIhUIh7j0SvuSzb6ztaOeM9b6SSCRR31fsMTYaTKPRjOp4jrbNfjbZUCgUNOs4TzIyMlBfX0874XnAJgT705/+hIcffviCvivuRfPrr7+OgoICrF69WmhTEgav14v+/v6EmDg+XjCbzWAYJmkTnvAV44QQtLa2YunSpUhLS4v5MxOJ6vG22bnBY20sBYNBEEJG2cEK6LFEdeT+yBJtP3uM9RawS/YYwzAYGhrC3Llz4fV6uX1CC/l4xePxQKFQCG0GZRaiVCrh8XiENiMuCReo7HpKSgr6+/shk8m4Y+Eiln2uRx6LPG+87ch9kccjmUwnKhuGPFHnbORnwp/hseByuaDX61FQUDDF/53ZSXZ2Njo7O6lojhGJRMLVGRXNsfOVr3wFDz74IL797W9fUAdV3ItmmgCMP93d3TAYDDRRAA96enqmJTR7tsLOqc1nqqnp8pKPx0Reilgbdey23+8fs8EYa8Px0KFDY9oZLqLDS+S+8O2x1sfbx4rzsY5PZn2sbXYf+/+eLF6vl4pmyrQgl8unRDQTQrjCiszIEr6fz/pklhPti7Yd+bmxGBgYQEtLS9QOw1g6GNkEUNHOGasDM9p6PGMymWA2m6lojpGsrCwcO3aMdpTyIDc3F3V1dVi0aBFts8bIVCUEi2vR3NHRgZqaGrz77rtCm5JQsKHZlNgghKCvrw/Lli0T2pSEwWw2IzU1Ne7DZ1mPr1gsFnRMXk9PD06fPo0tW7aM2WiNtYEbrTEcuc4K+Yka23wb8ADGbVyHEymm+RSv14tQKIR9+/aN8MRHOz/82FjnRTseaWu0Y+MtJ9oXuR5tX7TGTyyNInZsZHd397j3ZLT/21j7I/eFb0c7NtY5sSwnOhbt3LHOi7aPLcFgEB6PB3v27BlxHnt9j7Udfs1P9vqfTAdVtM4vdslGtYzXoTbRsWj72NLT04PGxkZs2bJlwt9MOS+aW1tbQQihgiYGFAoFdDod+vv7kZeXJ7Q5CQE7VMJsNscc6ZfsSKVSfP7zn8cbb7wxe0Xzu+++i/Xr1/PyZiU7brcbVquVzmfNA7vdDp/PJ9iYrUTEbDbT+uKBzWZDSkrKKG9sIsPHy8a3tLa2gmEY5ObmTiiGJtoXeTza+dE+E35svOVk1sfbN5njbW1t4zbU+R4bT/hPtmOBXY/sqIhcjhW5EGtHyHgdMqFQCPX19SgqKoJUKo2pEyfShvGiLyJtTHRSUlJgt9upCIwRg8EAv98Ph8NB5wKPkYyMDPT19VHRHCMikQg5OTno7OykopkHV155JW6++WaEQqFJt8HiWjS/8847uOKKK4Q2I6Ho7e1FamoqzTrLg/7+fphMprj3msYLhBBYLBY6BokHdrt91s3PPJ0dAJ2dnTAYDDRiJkb8fj/ee+89rF27NumyHE+GhoYGGAwGKmpigB3m5XQ6odFoBLYm/hGLxUhNTYXZbKbXV4xkZGSgubn5gsRMspGZmYna2lramcWDDRs2wOl0oq6uDitXrpzUd8Tt1el0OrFr1y4qmnnS19eHzMxMoc1IKPr6+pCeni60GQmDy+WCz+ejSSh4wHqaKbHh8/mmZIo2CmUs5HI5nXYqRkQiEVJSUmCz2YQ2JWFITU2F1WoV2oyEQa/Xg2EYWmc8MBqNCAaDGB4eFtqUhEEqlWL79u3YsWPHpL8jbkXzrl27MGfOHDqBNw8CgQAGBgaQkZEhtCkJg9/vh8VioXXGA6vVCp1ORz3zMRIKheBwOGadp3k68Xq9NFqGMm3IZDL4fD6hzUgY2BBtSmwYDAYqAHnAMAzS09PR19cntCkJg0gkQnp6Onp7e4U2JaG48sor8c4770z683Ermnfs2IErrriChh3wwGw2Q6FQ0BAqHpjNZqhUKpppnAdWqxUGg0FoMxIGl8sFhmGgUqmENiVhoKKZMp1MVQbtZEGj0cDhcAhtRsJgMBjgcDhoxwwPMjIy0N/fL7QZCQU7FpwSO5dddhmOHj2Krq6uSX0+LkVzKBTCjh07cOWVVwptSkLR19eHjIwM2tHAA4vFQhMp8ISKZn44HA6o1Wp6X8YIIYSGZ1OmFepp5gcVzfyQy+VQq9UYGhoS2pSEwWQyYXh4GH6/X2hTEoaMjAwMDw/TDkAeGI1GrF27dtKzMsWlaK6vr4fT6cSGDRuENiVhYKdNouOZ+WGxWGh2dh6wY2ioaI4dp9NJIxl4wDaaqGimTBdUNPNDrVbD6XTGNNUW5TwGgwGDg4NCm5EwKBQKqNVqWmc8kMvl0Ov11NvMkyuuuGLS45rjUjS/88472L59O2008YCdNsloNAptSsIQCAQwPDxM64wHNpsNYrGYikAeOBwOOmSCBz6fDyKRiI6Zp0wbMpmMerR4oFar4ff7aUcDD/R6PfU08yQ1NRUWi0VoMxIKGtbOnyuvvBIfffQR3G4378/GpWhmxzNTYsdiscBgMNCGJg+sVisUCgUda8oDm80GnU5HQ415QD3N/GBDs+k1RpkuqKeZH1KpFHK5HE6nU2hTEgadTkczG/PEaDRSTzNPTCYTLBYLjQLhwYIFC5CZmYmPP/6Y92fjTjR3d3fjyJEjuOyyy4Q2JaEYHBykHlOeWCwWWmc8GR4eplmgeUJFMz/oeGbKdENFM3/YEG1KbGi1Wng8Hnqd8cBoNMJqtSIYDAptSsKg1+vh9/vpvckDhmEmnUU77kTzxx9/jPLycphMJqFNSSjo2Fz+0Drjj81mo6KZB4QQuN1uGs3AA7/fD6lUKrQZlFmMVCql4dk8UalUcLlcQpuRMMhkMiiVSjq/NQ/UajWkUikNa+eBWCyGXq+nHnqebN++Hbt27eL9ubgTzbW1taioqBDajITC5XLB4/FQAcgDQgiGhoZoQiseEEK48GxKbHg8HhBCoFQqhTYlYaCimTLdUNHMH6VSOakxgMmMVqulopkHDMPAYDBQ0cwTOhacP6tWrUJTUxPvIRRxKZrLy8uFNiOhGBwchE6ng0QiEdqUhMHlciEYDFKvKQ88Hg/8fj9SUlKENiVhcLlcUCgUEIni7lEbt1DRTJluWNFMxwHGDvU084eKZv7QseD8oWPB+WMymZCXl4e6ujpen4urllwoFEJ9fT1WrFghtCkJBQ0z5s/w8DBSUlKomOGB3W6HWq2myeZ44Ha7qZeZJ1Q0U6YbqVQKQggdO8kDKpr5k5KSQkUzT7RaLRXNPElNTYXD4YDX6xXalIRixYoVqK2t5fWZuFIMjY2NCAQCWLhwodCmJBRWq5WKZp4MDw/TMGOe0KmT+ONyueh4Zp4EAgEaNUOZVtjri4Zoxw4bnk2987Gj0Whogiae6PV62O122qHFA5lMhpSUFOpt5kl5eXlii+a6ujosW7aMNph4EAqFYLfbqQDkCRXN/KFZoPnj8XigUCiENiOhoJ5mynTDzgNORXPsKBQKhEIhWmc8UKvV8Pl8tM54oFQqIZFIYLfbhTYloaBDAfhTXl6e2OHZdDwzfxwOBxiGoWKGJ1Q084eKZv54vV4qmnlCPc2UmUAikVBvFg8kEgnEYjE8Ho/QpiQMMpkMMpkMDodDaFMSBoZh6LjmSUBFM3/Ky8tx9uxZXvVGRXOCY7fbkZKSAoZhhDYlYfD7/fB4PDQJGE9oeDZ/PB4P5HK50GYkFFQ0U2YCiUSCQCAgtBkJA8MwUCgUdNwkT+j81vxJSUmhnmaeUNHMn/T0dOTk5KC+vj7mz8SNaA6FQqirq6NJwHhC583lj9PphFQqhUwmE9qUhCEUCsHlclFPM09oeDZ/qGimzARUNPNHLpdTTzNPqGjmD60z/mi1WjidTho9wxO+ycDiRjQ3NTXB5/Nh0aJFQpuSUFDRzB/qMeWP1+ul8w3zhBBCw7MnQTAYpBnaKdMOFc38USgUVDTzhGYd549Go6Eh7TxRKpUQi8W03njCNxlY3Ijmuro6LF26lCaA4QkVzfyhY3P543a76XzDPAkGgwgGgzQ8myfU00yZCaho5o9cLofP5xPajIRCqVTSjgaeqNVquFwumqmdBwzD0BDtScA3GVjctIDp/Mz8CQQCcLlcSElJEdqUhIKKZv6wopkSO2zjknYE8iMQCFBPM2XaEYvFNJSRJzKZjIpmnrBTdVFiR6VSgRBC640ndCw4f8rLy3HmzJmYhwPEjWhub29HYWGh0GYkFG63GyKRiHqyeEJFM388Hg8NzeYJO3USTdIXO4QQhEIhKpop045IJKKimSdSqZSKZp7QkHb+iEQiqFQqGmrMEzoUgD8ZGRmQyWTo6uqK6fy4Ec3d3d3Izs4W2gzB+Mc//oFLLrkERqMRDMPgyJEjo87ZsmULGIbhilarxe9+97sRjfL29nZ89rOfhUqlQnp6Oh566KFRIWiPP/44cnNzsWHDBjQ2Nk73T5s2JqqzwcFB3HfffSgtLYVSqUReXh7uv/9+9Pf3jxDNyVRnY9HX14dbb70V2dnZUKlU2L59O86ePTvinKGhIfz85z+H0WiERqPB9ddfj76+vhHnvP3225g3bx5KS0uxY8eOmfwJgnHq1ClcddVV0Ol0UKvVWLVqFdrb2wH8x9N8zz330Hr7lO9///uYP38+1Go1DAYDtm3bhsOHD3PHCSGw2Wy4/fbbodVqodfrcfvtt49qPL3wwgvIz89HWVnZiM8nC8888wwKCgqgUCiwevVqVFdXc8fOnDmD9evXIzc3Fz/4wQ8EtFIYnnvuOSxduhRarRZarRZr167F+++/zx33eDy45557cNFFF6GsrCzp70mWrq4ufPGLX4TRaIRSqcSSJUtQU1PDHSeE4Ne//jUuv/xyKJVKbNu2bdR7orKyEsuXL0dBQQFefPHFmf4JgmC32/GNb3wD+fn5UCqVWLdu3Yj7UaFQ4M9//jOysrKStt4++eQTXHnllcjOzgbDMHjrrbe4Y36/H9/+9rexZMkSqNVqZGdn47//+7/hdDpHCMDBwUHcfPPNSfNeGK/OAODWW28doQcYhsFdd901wjufbHU2GRiGQXZ2Nrq7u2P7AIkT5s6dSz788EOhzRCMv/zlL+Txxx8nL7zwAgFA6uvrR52zefNmcuedd5Kenh7S09NDqqqqyL///W/ueCAQIIsXLybbtm0j9fX15L333iMmk4k8/PDD3Dn79+8nq1atIjU1NeSZZ54hF1988Uz8vGlhojo7duwYue6668jbb79NmpqayK5du8jcuXPJ2rVridPpJIQkX51FEgqFyJo1a8jGjRtJVVUVOX36NLnrrrtIXl4ecTgc3HnXX389ycrKIrt27SI1NTVkzZo1ZN26ddxxj8dDcnNzyYcffkj+/e9/k9zcXOL1eoX4STNGU1MTSU1NJQ899BCpq6sjTU1N5F//+hfp6+sjhBDS2dlJrrrqKjJnzhxab5/y8ssvkw8//JCcO3eOHD9+nNx+++1Eq9WS/v5+QgghPp+PrFixgixdupQcOnSI7Nu3j5SUlJDPf/7z3He0tbWRkpIScvDgQfLGG2+QBQsWCPVzBOG1114jMpmM/OEPfyAnTpwgd955J9Hr9eRPf/oT8fl8ZNu2beTZZ58lNTU1ZOXKleTAgQNCmzyjvP322+Tdd98ljY2N5MyZM+SRRx4hUqmUHD9+nBBCyFe/+lUyZ84c8sILL5DXXnst6e9JQggZHBwk+fn55NZbbyWHDx8mzc3NZOfOnaSpqYk758c//jHRarXkiSeeIEePHiVXXXUVKSwsJG63mztnwYIF5O9//zs5cOAAKS4uJu3t7UL8nBnlhhtuIAsXLiR79+4lZ8+eJY899hjRarWks7OTEELIU089RdRqNXn11VeTtt7ee+898j//8z/kH//4BwFA/vnPf3LHhoaGyLZt28jrr79OTp8+TSorK0lFRQVZsGABOX36NHfe9u3bybJly5LmvTBenRFCyC233EK2b9/O6YGenh7S1NREdu7cyZ2TbHU2WdatW0deeeWVmM6NG9GsVqu5l1oy09LSMq5o/vrXv85tnzp1itTV1XHb7733HhGJRKS3t5fb99xzzxGtVsu99N955x1y9dVXE5/PRw4dOkRWrVo1bb9lphivziL561//SiQSCfF4PISQ5K0zljNnzhAAI+69YDBI0tLSyAsvvEAIOf9Sk0gk5Nlnn+XOOXXqFAFAKisrCSGEDA8Pk/z8fDIwMEAGBgZIQUEBsdlsM/tjZpgbb7yRfPGLX4x6/OjRo0QikZA33niD20frbSTDw8MEAPnoo48IIYTU19cTAOTw4cPcOe+//z5hGIZ0dXURQs53hq1cuZI4HA7S3NxMCgoKBLFdKCoqKsg999zDbQeDQZKdnU2+9KUvEZ/PR8rLy8nhw4eJz+cjV111FXn33XcFtDY+MBgM5Pe//z0ZGhoiUqmUvPHGG+TEiRPkyJEj9J4khHz7298mGzZsiHo8FAqRzMxM8vjjj5P333+fEHL+vSCXy8mrr77KnZeXl0eam5uJw+EgK1euJCdOnJh224XE5XIRsVhMduzYMWL/ihUryP/8z/9w9XbHHXcQi8VCCKH1NpYAjKSqqooA4K61kydPEgCkurqaOyeZ3gvRRPPVV189Yp/T6SRvvfUWCQaDSV9nfLj++uvJ//3f/8V0blyEZ9vtdjidzqQOz46Vl19+GSaTCYsXL8ZPfvKTEccqKyuxZMkSZGRkcPsuvfRS2Gw2nDhxgtv2eDxcGO5TTz01o/YLzeDgIFQqFTcOPNnrzOv1AsCIJF/sOPn9+/cDAGpraxEIBLB161bunPnz5yMvLw+VlZUAzs8ReNtttyErKwvZ2dm4++67Z3WCulAohHfffRfz5s3DpZdeivT0dKxevXpECNXRo0cRCASwbds2bl+y11s4Pp8Pzz//PHQ6HZYtWwbg/P2oVqtRUVHBnbdt2zaIRCIudGzx4sVYunQpdDodFi1alFQhyD6fD7W1tSOuKZFIhM985jM4c+YMAOCJJ57Atm3boFKpIBKJcOmllwplruAEg0G89tprcDqdWLt2LWpra+H3+7Ft2zYuERi9J8+Ho69cuRKf+9znkJ6ejrKyMrzwwgvc8ZaWFvT29uIzn/kMNw5cp9Nh9erVXL0BwKOPPooFCxZAp9NhzZo1WLhw4Yz/lpkkEAggGAyOSpKpVCqxf/9+rt4qKiq44Tq03iZmeHgYDMNAJpMBOP9e0Ov1WLlyJXcOfS8Ae/bsQXp6OkpLS3H33Xdzyaw8Hg+tMx5kZ2ejp6cnpnPjYl6P7u5uyOVy6PV6oU2Ja77whS8gPz8f2dnZaGhowDe/+U309PTggw8+AAD09vaOEH8AuO3e3l4A5xN5fPDBB+jv74der+ceSsmA2WzGT37yE1xxxRXcvmSvM7bB+PDDD+N3v/sd1Go1fvGLX6Czs5N7iPT29kIqlcJkMo34bEZGBldHAPDYY4/hG9/4BkQi0axvZPb398PhcODHP/4xfvCDH+AnP/kJPvjgA1x33XXYvXs3Nm/ejL6+Pkil0lHPtWSuNwDYsWMHbrrpJrhcLmRlZeHDDz/krq3e3t5R9SWRSJCamjqizl588UX87//+L1QqVVIlqDObzQgGg6OeWenp6VxD6PLLL8fAwABsNhvS0tKEMFNwjh07hrVr18Lj8UCj0eCf//wnFi5ciCNHjkAmk0Gv16O/v5+b0ibZ78nm5mY899xzeOCBB/DII4+guroa999/P2QyGW655RaubrKysmCxWEAIAcMwo+rt9ttvx0033QSfzweDwSDUz5kxUlJSsHbtWjz55JNYsGABMjIy8Oqrr6KyshIlJSVc3aSnp49IoJbs9TYeHo8H3/72t3H11VdzUw/29vYiPT19xHnJ/l7Yvn07rrvuOhQWFuLcuXN45JFH8NnPfhbf//734Xa7aZ3xICsrC8eOHYvp3LjwNPf09HCD3ZOBl19+GRqNhiv79u2L6XN33XUXLr30UixZsgQ333wzHnroIezcuRPnzp3jbUN6enpCib/J1hmLzWbDZz/7WZSUlOCuu+6alA2JVmdjEVmPhw4dwj/+8Q80NjYiNTUVKpUKu3fvxmWXXTZiTmZCSEy/XafTzcpGZmS9sV69q6++Gt/85jexfPlyfOc738EVV1yB3/72twDAKzPvbKy3aPfs1q1bceTIERw8eBDbt2/HDTfcgP7+fgDgNS8nm7CIMhq5XJ60ghkASktLceTIERw+fBh33303brnlFpw8eXLEOSKRCKFQKOp3zMZ7MhqhUAgrVqzAj370I5SVleGuu+7CnXfeyT3LWFgRM96zjU3ylyy89NJLIIQgJycHcrkcv/rVr/D5z39+xPtTKpXC7/eP+z3JVm9j4ff7ccMNN4AQgp///OdcJBwfkuW9cNNNN+Gqq67CkiVLcM0112DHjh2orq7GmTNneE/VlSx1Fo2E9DRnZWUJbcaMcdVVV2H16tXcdk5OzqS+Z+7cuQCApqYmFBcXIzMzE1VVVSPOYbOCZmZmTtLa+OBC6sxut2P79u1ISUnB008/PWL6h9lcZ2MxVj0qlUocOXIEw8PD8Pl8SEtLw+rVq7mwnvT0dG5OcK1Wy322r69vVtbRWETWW1paGiQSyagwugULFnBh7ampqfD7/RgaGhrhPU2Weot2z6rVapSUlKCkpARr1qzB3Llz8eKLL+Lhhx9Geno6hoeHR3xPIBDA4OBgUtTZRJhMJojF4lHZnvv7+5O+wR2OTCZDSUkJgPPzcFZXV+OXv/wlbrzxRvh8PgwNDY0QzclyT0YjKytrzGfZm2++CeA/70KLxQLg/D0pkUjQ19eH5cuXz6it8UZxcTH27t0Lp9MJm82GrKws3HjjjSgqKuLqzW63j/A003obDSuY29ra8PHHH0Mmk+H48eMAzl9/bMcqC30vjKSoqAgmkwm9vb3w+/20zniQlZUVc/bsuPE0J5NoTklJ4RqNJSUlk+7hYb1dbN2tXbsWx44dG3GjfPjhh9BqtQk/RmaydWaz2XDJJZdAJpPh7bffhkgkglQq5Y7P5jobi/HqUafTIS0tDWfPnkVNTQ2uvvpqAMDSpUshkUhGePfPnDmD9vZ2rF27dsZ/gxBE1ptOp8OqVau4e5ClsbER+fn5AIB58+ZBKpVi165d3PFkqrdY79lQKMR5FMrLy+FwOFBbW8sd//jjjxEKhUYI8GRFJpOhvLx8xDUVCoWwe/dulJaWCmhZfMNeY+Xl5dw9yYrmZLono7F+/fpxn2WFhYXIzMzE7t27IRKJEAgEYLPZcPjw4aSut3DUajWysrJgtVqxc+dOXH311Vy91dTUcJ5mWm+jYQXz2bNn8dFHH8FoNEIqlSIUCiEUCmHt2rUYGhqi74Vx6OzshMVi4ZwctM5iJysrK2ZPc1xkz37ggQfIfffdJ7QZgmKxWEh9fT159913CQDy2muvkfr6etLT00MIOT+9zRNPPEFqampIS0sL+ec//0kyMjJGZLxkp0+65JJLyJEjR8gHH3xA0tLSRkyfNJuYqM6Gh4fJ6tWryZIlS0hTUxPp6ekhH3/8Mdm9ezcJBAKEkOSrs7H429/+Rnbv3k3OnTtH3nrrLZKfn0+uu+467rjT6STbt28neXl55OOPPyY1NTVk7dq1ZO3atQJaLTz/+Mc/iFQqJc8//zw5e/Ys+fWvf03EYjHZt28fIYSQw4cPky984Qu03j7F4XCQhx9+mFRWVpLW1lZSU1NDbrvtNiKXy7ns7QMDA2TlypWkrKyMHD58mOzfv5/MnTt3xDQZyc5rr71G5HI5+dOf/kROnjxJ7rrrrhFTTiU73/nOd8jevXtJS0sLaWhoIN/5zncIwzDc9Ixf/epXSV5eHnnllVfI73//+6S+J1mqqqqIRCIhP/zhD8nZs2fJyy+/TFQqFfnrX//KnfPjH/+Y6PV68r3vfY8cPHiQXH311aOmTkpGPvjgA/L++++T5uZm8u9//5ssW7aMrF69mrsX2am6fvGLX5CGhoakrDe73U7q6+u52RF+/vOfk/r6etLW1sZl+c/NzSVHjhzhpk9qb28nb7zxBleP27dvT6r3wnh1ZrfbyYMPPkgqKytJS0sL+eijj8iKFSvI3LlzSVVVFZd9PdnqbLKYzWYCgNjt9gnPjQvR/PnPf5786Ec/EtoMQfnjH/9IAIwqjz32GCGEkPb2drJp0yaSmppK5HI5KS4uJtdccw0ZHBwc8T2tra3ksssuI0qlkphMJvKtb32L+P1+AX7R9DNRne3evXvM4wBIS0sL9z3JVGdj8ctf/pLk5uYSqVRK8vLyyHe/+90R85LabDby5ptvkq997WvEYDAQlUpFrr32Wq5zIpl58cUXSUlJCVEoFGTZsmXkrbfe4o5VVlaSU6dO0Xr7FLfbTa699lqSnZ1NZDIZycrKIldddRWpqqrizunv7ydvvvkm+fznP080Gg3RarXktttui+lllkz8+te/Jnl5eUQmk5GKigqyf/9+8tZbb1HRTAj58pe/TPLz84lMJiNpaWnkoosu4gQzIeevw6997WtEp9MRhUKR1PdkOO+88w5ZvHgxkcvlZP78+eT5558fcTwUCpHvfe97xGAwELlcTi666CJy5swZgayNH15//XVSVFREZDIZyczMJPfccw8ZGhrijodCIXLPPfdwbbdkrLdobbFbbrmFmzJ0rPLkk08Sl8tFCDnvJEmm98J4deZyucgll1xC0tLSiFQqJfn5+eTOO+8kvb295Pjx4+TIkSOEkOSrs8kSCoWITCYjjY2NE57LEMIj88o0sX37dlx77bX4yle+IrQpCYPD4cDu3btx5ZVXCm1KQlFdXQ2DwcCNd6NMzNDQEA4ePIjLL79caFMSioMHDyInJ4cLcaRMTH9/PxoaGkZMqUSZGL/fj/feew+XX375iOEnlOh0dnaipaUFGzduFNqUhGLnzp1YtWoVUlNThTYlYTh37hzMZjMNi+XJjh07sHnz5qRJyDcVNDY2wm63o7y8XGhTEoqsrCy89dZbE96jkxrT/Mwzz6CgoAAKhQKrV68ekUjpzJkzWL9+PXJzc2Oe8ysQCCR8VuKZxu/3c5ksKbETDAYhFouFNiOhCIVCtM4mQSgUGpFBlRIbyTKLAkVYGIbhla2dcp6Jso5TRsPOCU7hh0QiQSAQENqMhEIikUyYqZ0yGqlUGtO1xrtF9/rrr+OBBx7AY489hrq6OixbtgyXXnopl0jp3nvvxRe/+EX861//wr/+9S8cPHhwwu9kMzFSYicYDNI6mwT0WuNPIBCgonkSUNHMH/Lp/K8UynRDRfPkoKKZP1Q0Tw6xWExFM09iFX+UkcTaQcO7Rffzn/8cd955J2677TYsXLgQv/3tb6FSqfCHP/wBAGC1WlFeXo6lS5ciOzsbQ0NDE35nIBCgIWU8oY3LyUHrjT+EECr+JgG91vhD64wyk1DRzB/a2cAfkUhE62wS0HqjzBTT4mn2+Xyora0dMd5MJBJh27ZtqKysBAA88cQT2LZtG1QqFUQiES699NIJv5eGGlMoFAqFQpkpaOfM5KD1RqFQZhuxhrXzUqpmsxnBYBAZGRkj9mdkZOD06dMAgMsvvxwDAwOw2WxIS0uL6XtZdU/j8GMnEAiAEELrjCeEEASDQVpvPKDX2uSg1xp/6LU2Odj6ovUWO8FgkF5rk4AQgkAgQOuNB/Ramxz0WuNPMBhEKBSidcYTsVg89aI5VuRyecyCGTjfc1lbW0tDtCfBe++9J7QJCYfVasWRI0eENiPhoNcaf+rq6oQ2ISGh19rk+PDDD4U2IeGg1xp/ampqhDYhIaHXGn+qq6uFNiEhodcaP1wuV0zDEHmJZpPJBLFYjL6+vhH7+/r6kJmZyc/CcCMkEixbtoxOacMDs9mM48ePY8uWLUKbklBUVlYiPz8f2dnZQpuSMPT39+P06dPYtGmT0KYkFAcOHEBxcfEFPRuTjb6+PjQ2NtJpgHji9/vx4Ycf4uKLL6adzzHS19eHs2fPYsOGDUKbklDs27cPpaWlSE9PF9qUhKGnpwctLS1Yt26d0KYkFHv37sXChQt5OeGSna6uLrS3t2Pt2rVCm5JQPPLIIzENE+YlmmUyGcrLy7Fr1y5cc801AM5niN21axfuvffeSRkKnBfNhBD6sueBRCIBwzC0znjCMAzEYjGtNx6wDxJaZ/yg1xp/6HPtwpBKpbTuYkQkEtFrbZJIJBJa33MBOgAAzF1JREFUbzwQi8X0WpsEDMPQa40nYrEYIpGI1hlPYs2txTs8+4EHHsAtt9yClStXoqKiAk8//TScTiduu+22SRkK0LnYJgPNYDk5GIah02XwhGawnBy03vhDn2uUmYQmteIPzXDPHzr94OQIhUL0WuMJvT8nR6zT0fIWzTfeeCMGBgbw6KOPore3F8uXL8cHH3wwKjkYH6ho5g+ts8khkUjofIk8oXNMTg46nyl/qGimzBS0cTk5qADkTzAYpHU2CWIVMpT/QOtsckybaAaAe++994LCsSORSqXw+XxT9n3JAJsenb74+SEWi2lnA0+oaJ4cNKphclDRTJkJ6LtzclDRzJ9gMAixWCy0GQlHMBikApAndArfyeHz+WKqt7h48hmNRpjNZqHNSCikUikIIbRRzhPqaeYP9ZhODlpv/KGeZspMQUM/JwcVzfwJhUJUNPOEnbKRCkB+BAIBOp6ZJ6FQCBaLBSaTacJz4+LJl5WVhZ6eHqHNSCjYm4LOxcYPGtbOH9bTTMUMP6ho5g8VzZSZgnqaJwcVzfyhnmb+sO00Kpr5QT3N/LFYLAgEAsjKyprw3Lh48mVnZ6O7u1toMxIKkUgEkUhERTNPqGjmD/uypx56ftCwdv7Q5GmUmYIQQsXfJKACkD+0zvjDttNovfGDepr5093dDZ1OB5VKNeG5cfHGoJ7mySGVSqkA5Ak7FpwSOzSqYXLQDhr+UO88ZaagHlP+sBFH1JPFD7/fT4UMTwKBADd9EiV26LXGn56enpi8zECciObs7GwqmicBTaDGH7lcDq/XK7QZCYVIJKKdDZOAimb+UNFMmSmoaOYPGzlDvX/88Pl8kMlkQpuRUHg8HsjlcqHNSDh8Ph8VzTzp6elBdnZ2TOfGxRuD9TTTsDx+KJVKeDweoc1IKBQKBRXNk0Amk9EOGp7Q8Gz+UNFMmSmoaOYPDZmdHFQ088fr9UKhUAhtRsLh8XigVCqFNiOh6O7uTixPc1ZWFnw+HwYHB4U2JaFQKpVwuVxCm5FQyOVy2tEwCWhUA3+op5k/VDRTZgo6ppk/7FymNIEaP2jILH+8Xi/1NPMkFApR0TwJEs7TrNFokJKSQkO0eaJUKuF2u4U2I6FQKBQIBALUA8gTmUxGw7N5QkPa+cOKGCqcKdNNMBikopknrGim8IN6mvlDw7P543a7wTAM9dDzJOHGNAM0g/ZkoKKZPzKZDAzDUG8zT+hYcP7Qjgb+0EztlJmCZjTmDx0vyR9CCBXNk8Dj8VDxxxO32w2FQkEjQXjS3d2dWJ5mgGbQngwqlYqKZp4wDENDtCeBQqGgdcYTGtLOHyqaKTMFFc38oeKPPz6fD6FQiApAnlDRzB+32x3TtEmUkSSkp5mKZv6wnmaaQI0farUaTqdTaDMSCoVCQTtoeEI9zfwRiURgGIaKZsq0Q0Uzf/x+PxXNPHG73ZBKpTSsnScul4sKQJ643W46npknhJDEFM0FBQU4d+6c0GYkFEqlkhv4T4kdKpr5QzO184cVzXR8Lj9o1nHKTEBFM3+op5k/NDETf4LBIFwuFzQajdCmJBQul4teazzp6upCIBBAbm5uTOfHjWguKytDXV2d0GYkFGKxGGq1Gna7XWhTEgoqmvlDPc38Ycf+UW8zP6hopswENBEYf+iYZv6w40wpseNyuSASiWi98cRms0Gr1QptRkJRV1eHBQsWxNzZEDdvjPLychw7dowmG+KJVquFzWYT2oyEgopm/iiVSni9Xuo15YFEIoFYLKbPNJ7QqbooMwHNBM0fOg0Qf6inmT9OpxNqtZomtOIBIYSK5klQW1uL8vLymM+PG9FcWFgIjUaD48ePC21KQkFFM3/UajUcDgcdC84DNiMj9TbzgyZQ4w8VzZSZIBgMUtHME6/XS71/PKEhs/xhRTMldlwuF0KhEA1p50nCimaGYbBixQrU1tYKbUpCQUUzf9RqNQKBAM1szAOGYbjOBkrsKBQK6mnmCQ3PpswE1NPMHzp3Ln8cDgcVMjxxOBxUNPPEZrNBo9HQISc8SVjRDICK5kmQkpICu91Ow2Z5IJVKoVQqaWcDT2hYO3/o9Gb8oZ5mykxARTM/CCF0GqBJQL2m/KFhxvyhdcaf7u5u9PX1YdmyZTF/Jq5Ec3l5ORXNPGF7MKmY4YdOp8Pw8LDQZiQUGo2Gepp5Qj3N/KGimTITUNHMj0AgQOcb5onP54Pf76eimQfs2FydTie0KQkFFc38qa2txfz583lFgsSdaD527BgNm+UBwzDQarVUAPKEimb+UE8zf2jWcf5IpVKacZwyrYRCIQSDQZoJmgdutxtisZh2NPDA4XBALpfT64wHTqcTwWAQKSkpQpuSUAwPD1PRzJO6ujpeodlAnInm4uJiKJVKnDhxQmhTEorU1FQMDg4KbUZCQUUzf+iYZv4olUoqmnlCRTNlumGvLypmYsftdkOpVNKMxjygodn8YT2mdGxu7Hi9XjidTqSmpgptSkLBdzwzEGeimSYDmxypqamwWCxCm5FQ6HQ6OBwOmnCIB1qtFi6Xi4bO8kClUsHlcgltRkIhkUioaKZMK4FAAAzD0IY5D1wuF1QqldBmJBQ0ZJY/Q0NDNDSbJxaLBSkpKZDJZEKbklAkvGgGaDKwyWA0GmGz2WhDkwdKpRISiYQmA+OBXC6HTCajdcYDdn5r2jkTO1KplHbMUKYVv98PqVRKvaY8YD3NlNihopk/dDwzfwYHB2E0GoU2I6Ho7e1FT08Pli9fzutzcSeaV65ciaqqKqHNSCgUCgXUajUN0eYBwzDQ6/WwWq1Cm5IwsOPnqWiOHYVCAZFIREO0eUDDsynTDSuaKbFDPc38oeNM+UEIgdVqhV6vF9qUhMJisdDQbJ5UVVVh3rx5vMfOx51o/sxnPoOjR4+it7dXaFMSCjqumT9Go5GGtfOEimZ+MAwDpVJJQ7R5IJPJaDJIyrTi8/moaOYJ9TTzw+v1wuv1UtHMA7vdjmAwSEUzDwKBAIaHh6mnmSfvv/8+LrnkEt6fizvRnJ6ejlWrVuG9994T2pSEggpA/rB1RggR2pSEQafTUdHME5p1nB9SqZSKZsq04vP56Pg/ntCkVvyw2WxQqVS0c4YHg4ODMBgMNNcAD6xWK+RyOe3Q4gEhBDt27MAVV1zB+7NxeWVeeeWVeOedd4Q2I6EwGo2wWq10LCAPDAYDfD4f9QLygJ3ejHY0xA4VzfxgPc30GqNMF1Q088Pv98Pr9fKazzTZoaHZ/LFYLNRjyhOz2Qyj0UjzM/Dg6NGjGBoawubNm3l/Ni5F8xVXXIEPP/wQHo9HaFMSBrVaDYVCAbPZLLQpCYNYLIbBYKAeeh5otVqEQiE69RQPNBoNrS8eyGQyEEJoByBl2vD7/VQ088DhcEAmk9E64wEdm8sfOjaXP319fcjIyBDajIRix44duOSSSyCXy3l/Ni5F85IlS2A0GrFnzx6hTUkYGIZBZmYm+vr6hDYloaDTdfFDJBLRBGo8oZ5mfrDhjDQZGGW6oJ5mftDQbP5YrVYqAHngcrng8XhonfHA7XbDZrMhPT1daFMSinfeeQdXXnnlpD4bl6KZYRhcccUV2LFjh9CmJBQZGRno7e2lYY08MJlMMJvNtM54YDAYqGjmgUajgdPpRCgUEtqUhIBhGMhkMni9XqFNocxSqGjmBxXN/PB4PHC73dTTzAOz2QydTgeJRCK0KQlDf38/DAbDpDymyUpfXx9qa2tx+eWXT+rzcSmagf+Ma6ZiJnaMRiP8fj9N1MQDo9EIj8dDw2d5QEUzP5RKJRiGoWPneSCXy2kyMMq04fV6aUOTB3a7nY5n5oHVakVKSgpNAsYDGmbMn97eXlpnPHn33XexatWqSXvn41Y0b9myBRaLBcePHxfalIRBLBYjLS2NhmjzQCKRwGQyob+/X2hTEgaDwQCbzUbHnMaISCRCSkoK7czigVwup55myrRBRTM/7HY7TWrFA6vVCoPBILQZCUMoFMLAwAANM+ZBMBjEwMAAFc08mWzWbJa4Fc0KhQIXX3wxDdHmCR3XzJ/09HRaZzxQKpWQy+XU28yDlJQU2O12oc1IGKhopkwXhBB4vV4anh0joVCIimaeDA4O0rG5PLBarWAYhnY08MBisUAqldL7kgderxf//ve/Jz2eGQDievDAFVdcgRdffBEPP/yw0KYkDBkZGTh69Cg8Hg8UCoXQ5iQEGRkZOHnyJAKBAB1PEwMMw3BjwdPS0oQ2JyFISUnB8PCw0GZMGkIICCEIhULcengZa3+0c2MpHo8HPp8PYrF4xP5wW6Ltjzwn2jF2PXI53rHwZazHItdj2Z5of7Tz9u7dO+HUI9GOR+6PdTtyGcu+aJ+NdizW/dG2w0soFEIoFEJ/fz8GBwejnicSibjvYtcnOneifYk4LYzD4YBIJIJKpRLalIQgEAhgcHAQZWVlQpuSMPT19SE9PT0h7w+h6OnpQWZmJq0zHuzZswepqalYsmTJpL8jrhXCZz/7WXzlK19Bf38/DduIEYVCAaPRiK6uLhQXFwttTkKg0WigVCphNpuRmZkptDkJgclkQnt7u9BmJASEEGg0GnR0dMDn83GN9sjCCs3x9o+1znffWMuJ1iciVsEQa/F4PAgGgxCLxWN+/1hCJHJdJBKN+uxY5423b6wl33Mi6yna8VjF7FgEAgFUV1dj4cKF43b88RHnsYp+vp0Ik+m8GG+dvVbD943XycIOK2Gji8brvAn/7vE6gcLPG49o9wi7Pt4+kUg05r6xjsVyPLJEO2a1Wul4Zh4MDg5CoVDQTgYe9Pf30/YqD0KhELq6urB69WqhTUko2KzZF9LRENeiOTMzExs2bMBf//pXPPDAA0KbkzDk5uaipaWFPoR4kJ6ejt7eXiqaY8RkMuHo0aNx651nBWMwGBy1Huu+yDLefkLIiGWk6GV5//33ufWJGqzj7Ys8xjAMpFLphA3qWPbHuh4pTKeStrY2dHZ20kZBjLDTc6WlpdHkQxNgsVhQW1uLdevWTfl3jxdpMVan1ESdVnyWbPH7/eN2tkXroBvrnHDefvttAOdzpzAMM+5yLPE91v7Ifex2+P7IfeHH4tHLZjabYTKZ4tK2eIROm8Sf/v5+SCQSOgSABx6PB6+99hrefPPNC/qe+GvtRnDXXXfhiSeewDe/+U36EIqR7OxsNDQ0wG63IyUlRWhzEoLs7GxUV1dj6dKlnBCgREelUkGhUGBwcHDClx0rJMcrrPgcb1+kyB1vX2SDL7wxN1aDbKIGnFQqjXpOLA1IhmGwa9curFmzBqmpqfRZNgEKhYKOaaZMC9M5dCk8YoGNkkhUWAF/6NAhGI1G5OfnT6rTcKwOSr/fP25HZbTOzEiiieqJ9kWWyP0ikQgSiWTUeiwh9mazGQUFBdP0X5l9dHd3w2g00sR8POjs7ERubi5tR/Dgn//8J0wmEzZt2nRB3xP3ovm6667D/fffj08++QSbN28W2pyEQCqVIiMjA52dnViwYIHQ5iQERqMRDMPAbDYnVY8n610IBAIIBoPcMnJ9rH0ikQjHjh2DWq2eUBCHM15jZaz9UqkUCoUipgZRtHPi4eWi1+tht9thNBqFNiXuUSgU8Hg8QptBmYV4PB7aQI8BViDabDaUlpYKniOFFfGRQppPhyr7DvP5fBN22ob/HRY2uiZcUIvF4hGi2mq1QqVSwWazjXqvsedFbocv4+FdNZN0d3djzpw5QpuRMPj9fvT29l6w+Es2fve73+Guu+664Psr7kWzQqHALbfcgueff56KZh7k5ubixIkTmD9/ftI9hCcDwzDIzs5Gd3d33IpmVtyGF7YRECl6xzsn8rxwIl/s0V7yYrEYWq0WVqsVGRkZYwrfaGI4Wa9HvV6f0MnAZhK5XM55oxLdY0eJL2iSzNjxeDzwer1xkaE3ckjITBEpvtl3Z+R2MBiE1WqFVCqFWq1GIBCA1+uN2ukcvh4uzCOFePhyMuvhgj7ecLvdsFqtqKioENqUhKGnpwdqtTou7slE4cyZMzh06BD+/ve/X/B3xb1oBs6HaC9btgy//OUvYTKZhDYnIcjIyEB9fT2sVisd9xAjOTk5qKqqmrIQbdaL6/f7R4ndaPsiBW94CX+xjvVSjFxnPbSR+8dasoXPi9Xr9WLnzp3IzMyEUqm84Pqa7eh0Opw7d05oMxICuVzOJQRTq9VCm0OZRXg8HprYKkaGh4eh0WiSepw8G8EUC0NDQ5gzZw7vCL9IAR4prsfqFGcFebQO8kAgMGKYUnh7IZYilUqj7psqAd7d3Q2TyUQjP3jAhmZTYuf555/H9ddfPyX6MSFEc2lpKdasWYO//OUvNCFYjIjFYuTk5KC9vZ2K5hhJTU2FSCTiplJiha3f7+dKuOANF77RjrEvLTaka7wXkkqlGvclFi6M46HXWC6XQ6/Xo7+/H/n5+UKbE/fo9XrYbDaEQiE6bn4CGIbhQrSpaKZMJR6Ph06VFyNDQ0PQ6/VCm5EQEELQ19c3qammWGE+1Z0TkcJ7ouL1eqN26odHpYV3zPNdskUkEqGrqwt5eXlT+ptnMy6XC2azmU5nxgOPx4M///nPF5wAjCUhRDNAE4JNhoKCAuzfvx+LFi1Kup5iQsgIsevz+bixTGOJ4PBy6NChET207EM+2ktAqVRGPR4ueGcjGRkZ6Ovro6I5BtRqNUQiEWw2G22IxoBSqYTb7RbaDMosw+120/DsGLFarbSDIUZsNhv8fn9c5axgI8hkMtkFfxc7XdtY0XKRzgK32z1qP7vOJnRjGAaEEHi9XrS1tY0S1ROVeA05n05aW1uRkZFBI/t4MFUJwFgSRjTThGD80ev10Gq1aG9vT8jpp8KFb7jYHWt9LDEMgJuKJ7zIZDJO1LKCly0ejwcNDQ3YunUrF9qcbA9mPmRkZODs2bPUexoDDMPAYDBgcHCQiuYYoKKZMtUQQuB2u2mjMwYIIbBarZg3b57QpiQEfX19SEtLm7Ud5OFtqQuBHbJ25swZLsncWE4Mp9M5yqExXtuObddFLsfal4j/o2AwiLa2NpSXlwttSkIxVQnAWBJGNNOEYJOjsLAQZ86cQVFRkWDij8146fP5RhRW7I63DYx8OEY+/JRKJbRa7aiH5GR7IwkhaG5uxuDgIPWexoBOp4NUKk26rOOTJTU1FVarVWgzEgKFQkFFM2VKYecwpqJ5YpxOJwKBAHQ6ndCmJAR9fX00C3QMiEQiyGQy9Pb2YsmSJbwjGdiEqJGOknDnidfrhd1uH+VYYb3c7KwcMpmMK+HbkcfYfUI6Bnp6eiCVSmnkBw+mMgEYS8KIZgC48847sXz5cpoQjAfZ2dk4fvw4N053KmAFsNfrHSWEx9rn8/m4JFbRHkoajSbqw2smvb0MwyA/Px/t7e1UNMcAwzDIzMxET08PFc0xYDAY0NnZKbQZCYFSqYTZbBbaDMoswu12c+8UyvhYrVbodLqE9MrNNGwW6FWrVgltSkIwMDCAUCiEjIwM3p9lRfdkQs5ZL/d4jhqn0znqGCu2xxLTcrl81D52v1QqnbK2a0tLCwoLC2nkIw+ef/55XHfddVOqFxPqzTF//nysWbMGf/7zn/Gtb31LaHMSArFYjPz8fLS0tEQVzcFgEF6vlyvh4nesZfgDZKwHhkqlGvNhMpUPkOmEna7LbrcjJSVFaHPinuzsbNTW1mLp0qUJ8f8VEoPBAKfTCa/XSzOGToBKpaKeZsqU4nK5oFKphDYjIRgcHITBYBDajISgp6cHqampdKx8jLS3t2POnDkz7rkViUSQy+W8371jRUp6vV7Oqz08PDzKcRQKhcAwzChxHW2pUCiitpGHh4cxPDyM1atXT1VVzHqmOgEYS0KJZgC499578c1vfhP33nsvbXRGgU3YwM6xqFQqcfbsWRw7dgyhUGiEQGazJQLgbuBwsatSqaDX60fc4GwP2mwdwyqTyZCVlYX29nYsWrRIaHPiHpPJBEIILBYLjQCZADaqYnBwEFlZWUKbE9colUq4XC6hzaDMIlwuFw3NjpHBwUE6njlGuru7kZ2dLbQZCYHP50NPTw+2bNkitCkxIxaLoVQqY352sEMSozmfXC4XhoaGRjmjWJHNtrPZYrFYoNPpYLVaR+yfrW3wqeAPf/gDsrKypiwBGEvCiebrrrsOjz/+OP7whz/g7rvvFtqcGYW9CVkxHL4euY9NzBR+gw0MDCAzMxMajWbUTSmTyaiXMIy8vDzU1dVhwYIF9ME0AQzDIDs7m5tzkTI+JpMJFouFiuYJUKlU3Fi1ZMv+T5keqKc5Nnw+H2w2W1xlgo5XPB4PBgcHaYKmGOno6IBer5/VUXwMw3Azp8Q6ZSI75VdkcblcGB4ehk6nw4kTJzihDfzH0aVQKEYs2cJuJ1v73uv14kc/+hGefvrpKf/dCSeaRSIRHnvsMXzzm9/El7/85VnhbQ4Gg/B4PKMKK4DZwibGirw5FAoFNBrNqH3hY4GtVisOHDiAkpKSKZl+YLaTlpYGiUSC7u5uOpF8DGRlZaGurg5LlixJqofzZDAajWhqahLajLiHHXvKjkOlUC4Ut9tNhWAMWCwWrk1BGZ+enh4YDAYawRADhBC0tLSgtLRUaFPijmgi+8SJE0hPT8eaNWu4feERo5GOM6vVOmJfMBjkHGgKhYIrkdsKhWLWiOvf//73SE1NxXXXXTfl351wohk4721+4okn8Pvf/x733HOP0OZEhRACn88Hj8cDt9s95pIVwwzDjLqI1Wo1jEbjiP1yuXxSF7XBYEBqaiqam5sxf/78afi1swuGYVBYWIhz585R0RwDNEQ7doxGI2pra6kHdQIYhoFKpYLT6YRWqxXaHMoswOVy0ed5DFgsFtq5ECNdXV00NDtG+vv7EQgEkJOTI7QpCYHP50NrayvWrl07Yr9IJIo5XJwdqhnpjHM4HDCbzdz+QCAAhmE4raFUKkets8t4Tg7o8Xjwox/9CL/61a+mJUo0IUUz623++te/jttvv12Q3lB2Una3280VVgyHC+JQKASpVDrqwjMYDCP2zUQPz9y5c1FdXY3i4mLaWI+BvLw8nD59GoODg0hNTRXanLhGJBIhJycHHR0dVDRPgFKphFqthsViQWZmptDmxDUqlYqOa6ZMCYQQOJ3OmMMlkxmz2YySkhKhzYh7XC4XBgcHadbsGDl37hwKCwvpkLcYaWlpgU6nu6D2p0QigUajgUajGfc8Njw80rk3NDSE3t5ebh8hhEtcxmqYsYpQwvr3v/89TCYTrr322mn5/oQUzQBw7bXXct7me++9d8q/3+/3w+12w+VyjRDG4aI4FApBJpONuFB0Oh0yMjJG9NLEy/QWJpMJGo0Gra2tmDt3rtDmxD1SqRR5eXlobm6mojkG5syZg4MHD2LJkiVxc83HK0ajkYrmGFCr1XA6nUKbQZkF+Hw+BAIBOqZ5Avx+P4aHh6mnOQY6OjqQnp4+K4YJTjc2mw0WiwUrVqwQ2pSEIBAIoLm5ecbGyscyBpt1FrJOQVYPsV5rdjtSG4WLa5VKBZVKBYVCMeWOQo/Hg6eeegq//vWvp61jJmFbtqy3+b777sMdd9zBy9vMhk2zgtjlco1Yd7vd8Pv9kEgkI/7RGo0GaWlpcdGbMhkYhsG8efNw5MgRFBUVJZTtQlFUVISPP/4YbrebjlmaAL1eD4VCgd7eXhoCOQFpaWl0XHMMqNVq9Pb2Cm0GZRbgdDq5mR8o0TGbzdBoNPR9NwGEEHR0dGDBggVCm5IQtLS0ICcnh46Tj5G2tjaoVKqoU8UKQXj4djRYfRXpaLTZbOjr64PL5YLH4wHDMCP01VhLvhrl+eefR1paGq655poL/KXRSVjRDADXXHMNnnjiCTz//PO4//77Rxzz+/1wuVxwOp2cKGa33W43gsEgN6US+08ymUxcL4hSqUyYeYX5kJGRAblcjra2NhQVFQltTtyjVquRnp6OlpYWLFy4UGhz4hqGYTBnzhx0dHRQ0TwBaWlpqK2tpfM1TwD1NFOmCpo5Ozb6+/vjqqEer1itVni9XhotFAM+nw8dHR3YuHGj0KYkBMFgEE1NTQmZWJXNzySXy6HX68c8JxQKcWI63GE5ODg4wlstl8uhUqmgVqs5bcZuKxSKEd5kt9uNp556Cs8999y0hv8ntGhmvc1333031q9fj2AwyIljNslOeCVnZGSMEMXJGELKMAxKS0vR0NCAvLy8pKwDvpSUlODQoUOYO3cu9VJMwJw5c3D69GnqmZ8AuVwOnU6HgYEB2sEwDuyYZkJIwjUeKPEFHc8cGwMDA1i8eLHQZsQ9HR0dyMnJoRF7MdDc3AyDwQCdTie0KQlBS0sLZDLZrJ2WUiQSQa1WR30es2Hg4c5OVlSzIhsA5/BUq9V44403kJGRgauvvnpabU94xXTNNdfg+9//Pl5//XXcddddyM3N5SqRCpyxycrKQlNTE86dO0dT/8eA0WiETqdDc3Mzra8JUCqVMJlM6OjowLx584Q2J65JT09Hf38/Fc3jwHoG3W439RJSLggqmieGbZzSZI7jEwgE0NnZOSqrMWU0fr8fzc3NqKioENqUhMDv96OxsRErV65M2o7i8DDwsfIJsZ5qVlQPDg7ihRdewAsvvDDtdTZlPux//OMfuOSSS2A0GsEwDI4cOTLqnC1btoBhmBHlq1/96ohz3n77bcybNw+lpaXYsWPHhH+XYRg88cQTeOmll5CdnY3s7Gzo9XoqmMeBYRgsXLgQTU1N8Hq9QpuTEMybNw/nzp1DIBAQ2pS4p6CgAK2trSCECG1KXJOWlob+/n5aT+MgEomgUqngcDiENoWS4DgcjgkzyCY7AwMDSE1NpRFoE9DV1QWVSgWDwSC0KXFPS0sLUlJSaGK5GDl79ix0Oh0dIjEOrKc6LS0N+fn52LNnD3Jzc3HVVVdN/9+eqi9yOp3YsGEDfvKTn4x73p133omenh6u/O///i93zOv14p577sGzzz6L3/zmN7j77rvh8/1/9s47vq3q/P8fSba89x7y3ns7znASAmGVtIUySiAhtN9CGGW3pS0USlvKKPRbvuyuQKH8SumihZA9vffeU/KQLVl733t/f4R7Kzu24yS275V036/Xed0rWZYeSVfnnM/zPOc5lgu+9q5duxAXF3fB1+b5L6GhoQgJCUFfXx/bpjgEYWFh8PHxwcjICNumcJ7IyEiQJAm5XM62KZwmODgYNpsNGo2GbVM4ja+vLy+aeS4LiqJ40bwCpqenER4ezrYZnGdkZAQJCQkuGwlcKTabDYODg0hLS+M/qxVgNBoxNDSErKws/vNaITMzM3juuefw85//fF0+s1VzJ955550AcEFR4e3tvWThBLPZDJFIhIKCgnPGubnBbDZDLBYv+5wCgQBvvPEGtmzZgjvuuIPfTmmFZGVl4eTJk0hKSuLT1i4AvRa8paUFiYmJ/DqmZRAKhYiPj8fIyAgiIiLYNoeziEQihIeHY2pqil/rtQy8aOa5XCwWC6xWKz/OLQNBEJiZmeGrQV8AlUoFrVbLL6tZASMjI/Dy8uIdMSukt7cXkZGRfAbDRfD9738fW7duxbXXXrsur7fuO4x/8MEHCA0NRU5ODp588kkYDAbmb/7+/ti3bx+ioqIQHR2N/fv3w8/Pb0XPW1xcjH379uGBBx7g0x1XiL+/P2JiYtDT08O2KQ4BXXmcjzZfmPj4eExPTzMFG3gWJyIiAtPT02ybwWl40cxzufDbTV2Y2dlZiMXiFc+5XJWRkRHExsby19IFoCtAp6en81HTFaDRaDA+Po6MjAy2TXEYzp49i7/85S/49a9/vW6vua6i+fbbb8ef/vQnHD9+HE8++STef/993HHHHfMe85Of/ASzs7NQKBT43ve+d1HP/7Of/QwtLS345JNPVtNspyYjIwOTk5OYm5tj2xTOQ0eb+/v7+bXNF8Db2xvh4eG8g+ECREREQKVS8bUFloEXzTyXi1ar5cXgBZienkZkZCQvcJbBarVCKpUiISGBbVM4z/DwMDw8PPgtuVZIV1cX4uPj+SUkK8Rms2H//v348Y9/jPj4+HV73UsSzR988AF8fX2Zdvr06RX933e+8x1cffXVyM3Nxe7du/Hee+/h73//OwYHB+c9LiAg4JIGuKCgILz88st4+OGHodVqL/r/XRFvb28kJyejra2Nj9CvgKioKHh7e593zfKcT1JSEkZGRkAQBNumcBZPT08EBATw0eZl8PPzg9Fo5B1VPJcMv555eSiKwtTUFL+c5gKMjo4iICBgyf1nec5BV4DOzs7mnTArYGpqCkqlkt+d5SJ47bXXYLVa8eijj67r616SaN61axdaWlqYVlJSckkvXl5eDgAYGBi4pP9fjDvuuAPJycl49tlnV+05nZ3U1FRYLBaMjo6ybQrn4SuPr5ywsDB4eHhAKpWybQqniYyMxNTUFNtmcBaxWAyxWMw7QnkuGT7SvDwajQYWi4XfamoZSJLE0NAQkpOT2TaF8/AVoFcOQRBob29HVlYWPDw82DbHIZDJZPjJT36CN95444I1r1abSxLNfn5+SElJYZqXl9clvTi9LdVqbuAtEAjw+uuv44033kBHR8eqPa8z4+bmhtzcXHR1da2oWrmrw1ceXxkCgQDJyckYGBjgsxiWISoqCnK5nI+kLoFAIICvry8vmnkuGV40L8/ExAQiIiL4ApfLMDk5CYFAsKrzVWeErwB9cfT390MsFq9rirGj89hjj+GGG27A9u3b1/21V21Ns1KpREtLC7q6ugCcqwLX0tLCRFAGBwfx3HPPobGxESMjI/jXv/6FPXv2oLKyEnl5eatlBgAgJycHDzzwAPbv389P1ldIZGQkgoODme+PZ3kyMzMxMjIyr5Adz/nExsbCarXy208tg5+fH7y8vPjPaBn8/Pz4dc08l4TNZoPBYOBF8zJMTk4iOjqabTM4C0VRGBgYQFJSEi8EL0Bvby8iIiL4CtArQK/XY2BgAHl5efx1tUIOHz6Mzz//HC+//DIrr79qovlf//oXCgsLcf311wMAbrvtNhQWFuKtt94CcC7F7siRI9i5cycyMjLw2GOP4aabbsKnn366WibM4+mnn8bIyAjee++9NXl+ZyQ3NxdSqRRKpZJtUzhPQEAAYmJi0N3dzbYpnEYkEiExMZFfA74MdPRiYmKCbVM4i5+fHx9p5rkkdDod3N3d+dTHJdBqtdDr9fy2QMswNzcHnU6HuLg4tk3hNFqtFuPj4/y2ZSuAoii0tbVBIpHwDoYVYjabcf/99+O5555jLeNj1UTzXXfdBYqizmvPPPMMAEAikeDkyZNQKBQwmUzo7+/Hiy++CH9//9UyYR6+vr749a9/jSeeeIKvDL1CfHx8kJqayhcFWyF85fGVkZCQAKVSCZVKxbYpnIMkSQBAdHQ0pqam+KJpS+Dv7w+NRsO2GTwOiEajgb+/Px/JWYKJiQmEh4fzWygtQ39/PxISEvjP6AJ0dnYiLi6OL7q3AqampqBSqXgHw0Xw0ksvwcfHB/fddx9rNqz7Ps3ryY033oiioiI8+eSTbJviMKSkpMBms2FoaIhtUziPt7c3UlJSeCfDBfDw8EB8fLzLrwG32WxoaWnB22+/jX379iE7MwMlxUUgCAIBAQHw8PDgU7SXwN/fHwaDAVarlW1TeBwMjUbDp2Yvw+TkJL9OdxnUajVmZmb4AmAXgK4AzYvAC2O1WpniX+tdyMpRGRoawvPPP4833ngDbm5urNnh1KKZLgr24Ycf4rPPPmPbHIdAJBKhoKAA3d3d/BrCFZCamgqz2YyxsTG2TeE0KSkpmJ6edqlo4eTkJP7+97/j+9//PrZu2YIAf38UFhbi/vvuQ8vhg4i1mtDc0orf/e53EAgEiI6OhkwmY9tsTuLh4QEPDw8+RZvnoqEjzTzno9VqodVqedG8DH19fYiLi4OnpyfbpnAWgiDQ0dGBzMxMXgSugM7OTvj6+vLp/ivEZrPhjjvuwF133YWKigpWbWFPrq8TycnJeO2117Bv3z60tbXx+xCugNDQUMTHx6O5uRmbN2/m09qWQSQSIScnB21tbYiOjubTt5bAy8sLcXFx6O/vR3FxMdvmrDoWiwUtLS2orq5GdXUVaqqqMDp+bqut2MAAlEaF4+nNpSiPjUJhVDi8xeeuk31/P4gf//CHuOWWWxAbG4vTp0/DZrOx6knlKn5+ftBoNAgODmbbFB4HQqPR8PufLoFMJkNERAQ/bi2BVqvF1NQUduzYwbYpnGZwcBBubm5ISEhg2xTOMz09DZlMhu3bt/Nz6xXy3HPPQa1W46WXXmLbFOcXzQCwZ88efP7557jrrrvwn//8B0KhUwfYV4XMzEycOHGC35dwBURFRWF4eBg9PT3Izc1l2xzOkpKSgmPHjiE9Pd0h1zwRBIHTp09jcnISXl5eIEkStbW1qDpzBg2NjTCZzfBwd0NRdCRujI1A2YYClMdGITZg6dTQn+/YjH+8/kc888wzePXVV+Ht7Y2pqSnExsau4ztzDAICAlwqU4Hn8jGbzTCbzXykeREoioJUKkVWVhbbpnCW/v5+SCQSeHt7s20KZzEajejr60NFRQUvAi+A1WpFS0sLsrOz+WtqhZw5cwYvv/wyqqurOfGZuYRoFggEeOutt5Cfn4/XXnsNDz30ENsmcR43NzcUFhaiuroaERERDily1guBQIDc3FycPHkS8fHx/ARtCXx8fBATE4P+/n4UFhaybc6KoSgKr732Gp555pnzir4FeXthe4IEz23bgA2xUSiICofHRUSJo/198YPNZXj29dfxne98B7GxsZBKpbxoXgR/f3+Mjo6ybcYlY1/3gC6UufB8sccudnsh9Fpvs9nMFJdbDPtJ7cIJLn1bIBDMO1/ssY6CWq2Gt7c3H0ldBJVKBbPZzGffLYFer4dMJsMVV1zBtimcprOzE1FRUQgJCWHbFM7T0dEBPz8/fk/mFaJSqbB792784he/WPWtiS8VAeVCFYzOnDmDq6++GtXV1Zz5ArhOe3s75ubmsGXLFoedOK0XHR0dmJub41Pal0Gn0+H48ePYvn07Zx0xFosFTU1NOH36NM6cPo1jx45Bp9ef9zj6G/7olhvw9azUS349s82Ggjf/hIS8fPzjX//CsWPHcM0117jU2jCKokAQBEiSXLJptVq0tbWhtLQUFEXN+9tit+n7VnJc2Ba7n76Ptne5Zv8Y+twZWCioF7al7l/YhELhim/T58sd6bbw9uTkJHQ6HbKysubdv7CJRKJ578EV6OjogMViQVFREdumcJLGxkYIBAL+81kGuVyO+vp6XHHFFfDy8mLbHE4zPT2NhoYG/rNaIRRF4fbbb4dKpcJnn33Gmb7ZJSLNNJs3b8bjjz+Ob37zm2hoaOAv3BVAp2kPDg4iJSWFbXM4TUZGBo4fP46RkREkJiaybQ4n8fX1hUQiQU9PD0pKStg2h0GlUuGVV17B6VMnUVtbB6PJBG+xGGUxkRAsUbGZwjnh/NjBE9iVkQzRJS778HBzw6+ursTXPvwHDh48iIiICEilUiQlJV36G7oEaGFIEMSSbeHf7W/T5/bHxe5bTBwvJioXEzckSaKjowNubm5LCqWlBJdQKIS7u/uiwms5AWd/H3BhUbjwMfa36fOl7qNZ7vZikwer1YqDBw/immuuOS+qujDCvdi5/e3lRP9SjoGV3H8hx8Rizo6FDg6SJGG1Whe9f7HbBoMBAoEAjY2Nl3TNiUSi884XHulz+/sXni92e+Hf1hOSJCGVSnlBuAQajQYTExP8WuZloHeDyMrK4ufSF4BOy87JyeE/qxXy/vvv49ixY2hra+OMYAZcTDQDwFNPPYXDhw/j8ccfx+uvv862OZzHzc0NRUVFqKqqQmhoKAIDA9k2ibO4ubkhPz8f9fX1iIyM5DvHJUhPT8fRo0ehVqsREBDAtjkAgD//+c947rnnsCMpHs9UlmFzXAwKosJRNTaBqw58vOT/UQCkGi3OjMqwNVFyya9/XVoSrk9PxqMPP4wjx45hbGxsUdFMURRsNhtsNhsIgjjv3P641H2L3aabPRea9C9228PDY1lhsZQgWWnU78SJE0hLS0N0dPQlf9bOCC266M+Q5xzHjh1DVlYWIiMjz/sbnd2wXJbDSpxB9LnZbF6RY4n+7dmznKAWiURwc3Obd1zuPrrRt+0dPjRyuRxCoRBhYWFr+vk7Kj09PYiPj+fEGkqu0t3dDW9vb7741wWgKAotLS0ICAjgq2WvkIGBATzwwAP46KOPOLd8xOVEs5ubGz744AMUFBTgmmuuwQ033MC2SZwnODgYqampaGhowNatW/n1YcsQHh6OqKgotLa2ory8nFMeMq7g5eWFhIQEdHd3Y8OGDWybAwC49tpr4ebmhqtTEvDwxv9W957UnZ+WvRhLPY4CQAoEsIncYBOKzh1Fov+eC0WwiUQghCJ874H78Lf+UZw9exbBwcE4fvw4AJwnfGmEQuF5E+fFJtLu7u7w9PQ8byJt/5jFGhev3YCAAKhUKl4081wQm80GrVa7pGNOIBAwVerXe0yjo+jLObAWc3DZbDZYrVYYjcYlHWO0I23h+7QX1QaDAe7u7mhqapontOnm7u6+5H1c7BdWk7m5Ocjlclx55ZVsm8JZlEolRkdHsXXrVqe/Hi6X0dFRKBQKvlr2CrFardi9ezf27duH6667jm1zzsPlRDMAJCYm4o033sDdd9+NtrY2fo/CFZCWlobZ2Vm0tbU55ZZBq0lOTg6OHTsGmUzGF3RagtTUVBw+fBgKhYITBUQSEhJw55134ld//Rj3lObB68tJdKS/HwICAuDj47No8/b2hre3N4JzM9EQ4A+b8EtR/KU4tgrdQNFRQJKEG2mDG0FARBJwIwi4ETa4fXkeKBQh3UuMT//1L3znnnvg6emJxMTE8ya89G1XjCgGBgZiamqKbTN4HACNRgOxWMzJ/XXt0/7XQrDTaeq0gLYX1EajEa2trUhMTIRQKGT+bjabGVFu/38LRfhCEb3ccbHGdeHd3d2NpKQkTl43XIAkSbS0tCAtLQ1+fkvvDMFzrg/q6OhAeXk5PDw82DbHIXj22WdhNBrxwgsvsG3KorikaAaA3bt34/PPP8fevXtx8OBBl5yAXgwCgQDFxcU4fvw4xsbG+DSTZRCLxcjNzUVbWxvCw8NdqqDTSvHw8EBKSgq6urrWtHAaHc2xWq2wWCzzjnSjb+/evRuZmZn4IiwUvr6+sIncQBYIceCr34TZbIZer5/XDAYDDAYDhDYbEtwAsVYFN8IGd4JgxLEb8eWRtEG0gmJQiW42vHLmFP7s54vbvnk7wsPD+b7JjsDAQPT09ICiKE5PvHnYR6VSISgoyCWvE4FAwGSMLJysDw4OIjg4GBkZGSt+PvtlIbSoXuqo1+vn3WffaOxFtVgsPk9YL7zP/vZa9odyuRxzc3OcqrfBNfr7+yEQCPgaNxfAZrOhvr4eycnJ/DKIFXLy5Em8+uqrqK2t5azTymVFMwC8/vrrKCwsxAsvvIAnn3ySbXM4j6enJ4qKilBfX4+goCDey7gM0dHRkEqlaG1tRUlJiUtO3C5EcnIyRkZGMDk5uaJ0W5IkYbFYzmu08F3sttVqZbbgcXNzW3Iy5uXlhdDQUBw7dgxv/P1veO8rV8BXKIA7YcNnHT247aN/AjiXbk1jXz07Tb46vwVPdzf87zXb8JU/fYCvff1GTE1N8anIdvj7+zPpqfx6Q57lUKlUfA2OBVAUtWS9hOUQCARMn3mptTpo4W0vqBdzYOp0uvMcmvb9uH3fTTf7vtz+frqtJLpNURQ6OzuRnp7OO7qXQKVSob+/H5s2beKduRegvb0dHh4eSE9PZ9sUh2B6ehp33HEHXnzxReTk5LBtzpK4tGgOCAjAJ598gsrKSuTk5PDrm1dAREQEEhMT0dDQgMrKSohEIrZN4iQCgQD5+fk4fvw4pFIpJJJLLxLljNCVa5OSktDW1sZMqMxm86LCmE4dBMBETxZOkDw9PeHv73/eRIo+X4nj4tvf/jZefPFFfOgrxqObzkUbvp6RjI9uuQGPHjwOmUbHPDbG3w+/umbbZW03tRhXpSTgG9lp+Oc//oGQkBBeNNshEong7+8PlUrFi2aeZeHXvp/P3NwcDAYDYmJi1v217YX3pWAvtBdzjhqNRqjValitVpjNZubvdFaKWCyGh4fHoqLaw8MDc3NzsFgsiIqKAkmSvChcAEEQaGpqQkpKCoKCgtg2h9NIpVJMTk5i+/bt/HW0AsxmM2688UZs2bIF9913H9vmLItLi2YAKCwsxO9//3vs3r0bVVVVnPZwcIXMzEwoFAp0dHQgPz+fbXM4i6enJwoKCtDU1ISQkBCnn+TTFWQXa7TwtT+3T7Ftb29nBK+Hhwd8fHwQFBS06ARnLR01iYmJ2LdvH1768AN8uzgX/p7nUhu/npWKXRnJODMqw6ROjyhfH2yOj7nkbaYuxMtXb8XmD/+FXV/9KvR6PXx8fNbkdRyRwMBAzM3N8YKIZ0kuVATMVRkZGYFEImEKoDkS9Frqi4l0UxR1XibSQmesRqOB2WyGSqWCUCjEkSNHmNfz8PBghLb90dPTk/kb7cB19myyrq4uiEQipKWlsW0Kp9HpdGhtbUVxcTG/g8oKoCgK9957L6xWK373u99x/nfkeD3nGnDzzTejq6sLN9xwA+rq6vj1BxdAKBSipKQEJ0+eRGBgIOLj49k2ibNERUUhJiYGzc3N2LhxI+c7hIWQJMmIXZPJtOiRbnQkmJ5Y2Dc/Pz+EhoaeN/lwc3PD7Ows6urqUFxczIliGU8//TTeO3AA/1vThKe2VTD3i4TCy9pW6mKI8vPFE4VZqK2thY+PD66++up1eV1HIDAwEDKZjG0zeDiMSqWCp6cnP2m1w2KxQCaTYevWrWybsm7QEeYLpVt3dXVBKBRi06ZNoChqnnN34VGr1c4b9wiCgEAgOG/MsxfX9keuF0JbDLlcjtHRUWzbto2PnC6D1WpFbW0tEhISFt3mjud8XnnlFRw6dAj19fUO0V/zovlLnnrqKXR2duIb3/gGDh8+zK9puQDe3t4oKSlBbW0t/Pz8EBwczLZJnCUnJwcnTpzA4OAgZ4pn0FFhk8kEk8kEo9G4qCi2WCwAwKQ/208GgoODz5skiMXiix5Uw8LCEBoaip6eHk5kLsTGxuK+++/Hr998E/eW5iPM59IzBEgA4xBBBwF8QUECAiv9dL5Tko+9VQ2YzcuD0Wh0iAFlPQgODkZHRwdfDIxnSebm5vgU0gWMj48jMDAQ/v7+bJvCKQwGA4aGhpiClAKBAF5eXivub+llRQubyWSCQqGYN56SJMksL1oopmknD33Olei1xWJBc3MzcnJy4Ovry7Y5nIWiKDQ1NcHLywtZWVlsm+MQfPbZZ/jJT36CEydOOEzmmICiVlDS1UUwGAzYsmULioqK8M4773Ciw+I6g4OD6O/vx7Zt2zhb7Y4LKJVKVFVVYcuWLWuaMkhRFCwWCyOC6ePCZrFYGO84PUjbt4WD+lp7l3U6HU6cOIHNmzdzonjP7OwskpMSsTcrFb+6dvslPUcP3HAYntDayWQ/kLgKJmTAtsx//pf26VnU5VXAZDLh4YcfviQ7nA2KovCf//wHlZWVvAD4EqvVis8++wzXXXfduu85zEXq6uoQFBSE1NTVrTfgqFAUhaNHjyI9PZ2vr7GA2tpaiMViFBYWrunr0HU7FnNOL2w2mw1CoXDRsdleXHt5ea3pciWKotDY2AibzYby8nJ+TrwMPT09GB8fx9atW/mg2wro6upCRUUF3nnnHdx6661sm7Ni+EizHd7e3vjnP/+J0tJSvPbaa/jud7/LtkmcJykpCWq1GnV1ddi0aRNfGGwJgoODkZKSgoaGBmzduvWS1pTRaWNGo5Fp9sKYPpIkyVQ5pQdWLy8vBAUFnSeMuTII+vr6Ijk5Ga2traisrGTdrtDQUHzv+z/Asz/5Ce4vL0RScOBF/X8P3PA3nB+p0EKAv8ELN8K4IuGcGxGKM72dsEZJ0NnZiezs7IuywxkRCAQICgqCUqnkRTPPeVAUBaVSieTkZLZN4QxyuRw2m81hojnrxdTUFBQKBXbs2LHmr2VfCO1CO4/Q4nphU6vVmJqaYsZ7iqKYLDD78d5+3Pfy8rrkNexjY2OYnZ3Ftm3bWB+Tuczk5CQGBgZQWVnJC+YVoFAocMMNN+Dhhx92KMEM8JHmRamtrcWOHTvwt7/9DTt37mTbHM5DEATOnDmDgIAAFBQUsG0OZ6EoClVVVczWXQsHIboCqH0zGAwwmUwwGAzMIElHgOkBceE5vW7K0SAIAseOHUNqaioSEhLYNgcGgwGpycnYHBqIP33juhX/HwngdfhCCwH+uzGVPRT8QeE+6FaUqq2nKBzKLMdHf/4zPv7kE94xhXNearPZvObRIUeBjzT/F6PRiMOHD+O6665zyH5wLaiurkZQUNBF7c3s7HBtvLlYaCf6Qsf5wnOSJCEWi+eJaC8vL3h7e8+bNyycj6jVapw+fRrl5eV8nZ9l0Gq1OHXqFAoLC3mn1AqwWq3YuXMnQkJC8Je//MXh1sjzI8oilJeX4+2338att96Kmpoafp+1CyASiVBWVoaTJ09iZGTEIQeg9cBmsyE1NRX19fVobGyEp6cnDAYD06xWK0Qi0bxBzdfXF2FhYfMGO2cVTSKRCLm5uWhqakJUVBTrRcG8vb3x3M9/jm9961v47oZClMVGrej/xiGal5J9PgJoIMA4RIgHccHn8xEI4DcxhoSkJLz22mt8mjaAoKAgdHd3s20GDwehMxB4wXwOrVaL2dlZ3sG0gL6+PojFYoctZCoQCBgn+VJLmuyXa9GOd6PRCJVKhcnJSRgMBpjNZmYdt7e3N7y9veHh4YHR0VFER0fDx8eH34JrCejCX0lJSbxgXgEUReHBBx+ESqXCv//9b4e8pvhRZQl2796Nzs5O3HDDDaitreWLilwALy8vlJaWorq6Gt7e3ggPD2fbpHWHIAhGAOv1+nlHg8EAm80GNzc3iMViyGQyxMTEICQkBLGxscxgxZXiH2wRGRmJkJAQdHV1cWKSt3fvXvzvq6/iiUOncGLfLSv6bnSLRpcv/XEAsMGshmnTJjzw3e9i165dSEpKWvH/OiPBwcHQarWwWCx8OhzPPJRKJV+Y0o6hoSFER0fzNUfs0Ol0GBwcZIp/OSv2Vb2XEtYkSTKimm7j4+MgSRKzs7M4fPgwI9DpeYqPj8+8I5eWeq0XJEmivr4evr6+fAbHCnnjjTfwj3/8A3V1dQ67jSYvmpfhZz/7Gbq6unDLLbfgs88+c/m0twsREhKC/Px81NfXY/PmzU63RybttdXr9UyzF8YmkwlCoXDegBIaGsoMNLQoBs4VjZBKpcjPz+evqwXk5ubi2LFjiIuLQ0hICKu2iEQivPLrX+PKK6/Ex519uCXnwlknvljZipeVPg4AfM1GhGpV+Oo11+Db37obR44ec0gv7Wrh4eEBX19fKJVKfmsPnnkoFAq+ANiXWK1WjI+PY9OmTWybwhkoikJbWxvi4uI4UXSSbeg5Cy1iRkZGQJIkduzYAU9PT5AkySwRo5tOp4NcLoder4fZbIZIJJonoulz+razZcdRFIWWlhZYLBZs2rTJ5RwGl8Lhw4fxve99D0eOHEFcXBzb5lwyvGheBqFQiPfffx+VlZXYu3cv3n//faf78a82EokEBoMBNTU1qKysdLhtciiKgslkmieM7ZvNZoOHhwczIPj4+CAsLIwZJFbqcU1PT4dCoUBrayuKi4v5TtcOb29vZGRkoLm5Gdu3b2f9N7djxw7c8JWv4IdHz+AraUnwFi/v5JCAgB/IC65plqwgNdueFMUErr56J36/Zy/eeecd3HvvvRf1/85GcHAwL5p55mGz2aDRaPhI85eMjIwgICCAz5SzY2xsDFqtFqWlpWybwjlUKhU6OjpQXl7OZCYIhUJGCC+GzWabJ6j1ej1mZ2cxOjoKvV4PkiTh5eUFHx8f+Pr6zps7+fj4sD6+Xwq9vb2YnZ3Fli1b+KDHCqitrcVNN92Et99+GxUVFWybc1nwovkC+Pn54YsvvkBlZSX279+Pt99+mxc4FyAtLY0Rzps3b+Zkp2KxWKDT6aDT6aDX6+edEwTBdPI+Pj4IDAxETEwMc3s11soJBAIUFxfj5MmTnNq/mSskJSVBJpOht7eXE3sevvLqq8jKysSvqhrw1LblO30hgKtg+rJ6NoX5wvlcdPlKmFa8XzNNiE4Ff8KKp+66E088/jiuueYal64fEBwcjPHxcbbN4OEQc3NzTGFEV4cgCAwODvLFOe0wmUzo7OxEUVERJ+clbGI2m1FXV4fU1NSLKvzl5uYGf3//RXcyWBiE0Ol0UCqVGB8fZ+Zanp6e8PX1ndfoCDUXs6nGxsYwODiILVu28P3MCmhra8O1116Ln//857jjjjvYNuey4UXzCggPD8eRI0ewefNmPP7443j55Zd54bwMAoEA+fn5qK2tRX19PTZs2MBK50eS5DxBbN8sFgsTMfb19UVgYCBiY2PX1fvp6emJsrIynD17Fv7+/i65DnwphEIhCgsLcerUKURHR7OeRpeSkoJHHnkUL736KvYUZCM+cPmtjjJgw40wfrlP83/7Cn9QuPIi9mm2RwAgSS6FefsOhP71b/jWvn04fPQoJycW60FISAja2tpAEIRDRit4Vh+FQsFHmb9EKpVCLBYjIiKCbVM4AUVRaG1tRUREBJ+dsgCSJNHQ0ICgoCCkpaWt2vPSBca8vLwQGho672909W/7Odrs7CxGRkag1+sBgJmf2Tc/Pz/W6ljI5XK0tbWhvLyc3+5wBfT19WHnzp14/PHH8eCDD7JtzqrAbzl1EdDepXvvvRdPP/002+ZwHqvVijNnziAwMBAFBQVr5miw2WzQ6XTQarXMUavVQq/XQyAQnNfp0o0rnuaxsTF0dHSgsrISvr6+bJvDKXp6ejA1NYXKykrWxaFWq0VGWhrKgv3wl1tuWNH/kDhXTVsHAXy/TMm+nHdBAjiZWQprSwO+8vyv8H//93+4//77L+MZHReKovDFF1+gtLSU9bXvbMNvOXWOs2fPIiYmxqUzMIBzv42jR48iLS3NodcPriYymQxtbW244oorWN+ZgWu0tbVBoVBgy5YtnKg6TxcnWyzgYTKZIBaL4efnBz8/P0ZI+/n5Lbp11mqhVqtx5swZ5OXlQSKRrMlrOBNjY2PYvHkzbr/9djz//PNOE2hk/9fhQCQnJ+Pw4cOorKyEn58fHnnkEbZN4jTu7u7YsGEDTp06hd7e3suuMGiz2RhBrNFomHOj0Qh3d3em8wwODkZcXBz8/Pzg7e3N+R9rXFwcNBoN6urq+DUyC0hLS8PExAT6+/tZ3/rNz88PL7/yCm6//XYcGhjBzpSEC/6PEFjRtlIrRQggcUaK8bxC3FOSj+898QSuvvpql0zvFwgECAkJwezsrMuLZp5z6chKpRJ5eXlsm8I6k5OTIAgCsbGxbJvCCSwWC9rb25Gbm8sL5gWMjo5CKpVi69atnBDMwPziZAszJaxW67wAyezsLIaHh6HX6yESiRghTaeM+/n5wcvL67LmgfRyw5SUFF4wr4CpqSns2LEDX/nKV5xKMAO8aL5osrOzcfDgQVx55ZXw8/PDt7/9bbZN4jReXl6oqKjAmTNn4O7ujuTk5Av+D0mS0Ol00Gg0jDjWaDQwGAxwd3dnOsKIiAikpKTAz8/P4bc8yMrKgkajQVNTE8rKyhz6vawmdJr22bNnERkZyXpF9ttuuw3vvvM2Hj54Ak333AFP9/XvQiWKaQxExOOJb3wNh4d/g7133olTZ864ZIpyaGgoJicn2TaDhwOoVCrGeerKUBSF/v5+JCcns56dwwXotOygoCDExMSwbQ6nUCqVaG9vR3l5ucNsAeTu7o7g4ODzlmEQBAG9Xs8EU1QqFcbGxuaJaXruSB9XMm80mUyoqqpCZGTkqqauOytKpRI7d+7Ehg0b8H//939ON5flRfMlUFpaik8//RTXXXcd/Pz8cOutt7JtEqfx9/dHRUUFqqqq4Obmhvj4eAD/LRKh0WigVqsZkazT6SAUCpmOLTQ0FImJifD393d4cbwUQqEQJSUlOHny5KpE5Z2JoKAgpKSkoKmpCZWVlayKQ4FAgDfefAt5ubl48Uwdnt6+cd1tEFEkEmdkkEqS8NtdO7Hjj3/Byy+/jO9///vrbgvbhISEoLOzEyRJ8gLBxaEzDpxxfLgYZmZmYDAYXD5FnUYmk2F2dhZXXHGFy18b9hiNRtTV1SEzM/OiCn9xFZFItGhBMoIgmKi0RqPB7OwshoaGYDAYIBaLmf8JCAhg5pz0HMNisaC6uhpBQUHIy8vjr58LoNVqce211yI5ORl/+MMfnHJM5kXzJVJZWYm//vWvuOmmm+Dj44OvfOUrbJvEafz9/ZGdnY3W1lYmdUyj0cBiscxLpYmNjYW/v/9lp9M4ImKxGOXl5Th9+jR8fHz4NCA70tLSMD09jZ6eHmRnZ7NqS0ZGBh5/4gm8+NJLuDU3A+mh6194KH52AkPhsSjMzsajG4vx1FM/xjXXXIP8/Px1t4VN6AmOSqXiC0C5OAqFwuULPFEUhd7eXiQnJ3Mm1ZZNjEYjWltbUVRUxKdl22G1WlFTU4OIiAgkJSWxbc6aIhKJEBAQcF6WGr3cjw7ajI2NQa1WgyAIZqmfUqmEl5cXMjMzWbLecTAajdi1axcCAgLw0UcfOW3/wxcCu0z++te/4q677sKnn36K7du3s20OJ6D3ylSpVFCr1UwUWSQSwcvLCzqdDgkJCZBIJPDz83PaH9elIpfLUVdXh/LycqfwAK8WWq0WJ0+exIYNG86rxLneGI1G5GRlQSIgcWjPTaw4eAYiJJj1C0JhTxPK3/0ztO4e6OvvZ/bXdBXq6+vh7+/P+pp3NnH1QmAEQeDzzz9HZWWlS1e1nZmZQX19Pa666iqXvA7soSgKVVVV8Pb2RmFhIdvmcAaSJFFbWwuKoljb2YSrUBQFo9EIlUqFzs5O2Gw2uLu7Q6/Xw8PDAwEBAQgMDGSOrhjcWQyLxYIbb7wRc3NzOHTokMOk+l8KvFq5TL7xjW9Ap9Phq1/9Kg4dOoQNGzawbdK6YrVaGWFMi2StVjuvg4mIiEBAQABTlGtiYgJNTU2IioriBfMihIeHIzc3F/X19di8ebNLTwLt8fPzQ1ZWFpqamrB9+3ZWJ4VeXl546513sHPnThxo7sRdRTnr9toURWFUpUFz7xDEX70V36luR9f0DIBzDhdXq5YbGhqKiYkJlxbNrs7c3Bzc3Nzg5+fHtimsQkeZXV0wA2BScMvKytg2hTNQFIW2tjaYTCZs3ryZF8wLEAgE8PT0xNjYGDw9PVFRUQE3N7fzAkHT09PQaDRwc3M7T0j7+Pi4lJAmCAJ33nknJiYmcOzYMacWzAAvmleFu+66CzqdDtdccw3+/e9/Y/PmzWybtCYQBMGI47m5OahUKuh0Onh6eiIwMBCBgYGIiYlBQEDAsqX/o6OjYbPZUFdXh4qKCj6tchHi4+OZio2VlZUuFz1cisTERExNTaGtrQ1FRUWsDk5XXXUV7rzjDnzvb5/gmtRERPqtzWChMppQL5tCnWwK9bIp1E/KMaPVAQDu8QvD1ptvRcHOa7Bx40aXE8wAEBYWho6ODn6/ZhdmZmYGYWFhLjVZXcjs7CzUajXKy8vZNoV1NBoNuru7UVFRwTsQ7Ojv78f09DQqKyv5z2URKIpCY2MjTCYTNm3axAR13Nzczis+RhAEU3BMrVZjcHAQGo0GQqGQmQ8HBQUhKChoTbfCYhOr1Yo9e/agvb0dJ0+eRGBgINsmrTm8aF4lHnjgAbi7u+Paa6/Fxx9/jGuuuYZtky4LiqKg1WoZcTw3NweNRgN3d3emM4iJiUFQUNAlrRWKi4uDzWZDdXU1L5yXICMjgxHOmzdv5qPyOOcJLioqwvHjxzE+Ps66SHzl1Vdx8PPP8fDB4/jo5suva0CQJDrlCtRJJ1EjnUTtxDR65bMAgEB/f5SVl+OeW25HeXk5ysrKEBQUhMOHD+PGG2902VR+Hx8fiMViKBQKhIeHs20ODwvMzs6y3hewCUVR6O7uRkpKisuLIZvNhvr6eiQnJ/Nb0dkhlUrR39+PzZs3w8vLi21zOAdJkmhqaoJWq8WmTZsu+DsSiUSMOLZ/Dvt5c29vL7RaLcRiMYKCgpi5c2BgIMRi8Rq/o7XFYDDg5ptvxsTEBE6cOOEy8w9+Fr6K3HPPPfD398dNN92EP/7xj7j55pvZNmnFWCwWKJVKzM3NMUcATKeQmpqKwMDAVd33mC5AUV1djQ0bNvAD3AIEAgEKCwtRXV2NhoYGlJWV8elUADw9PVFcXIy6ujoEBQWxmpIZGhqK115/Hbfddhv+lt2HG7MubksKldGEWukkqscnUCubQp1sGlqTCSKRCHk52bjippvxZHk5NmzYgNTU1EW//5SUFHR3dyM0NNQpvdkXQiAQICwsDLOzs7xodkGsVivm5uZQXFzMtimsIZfLodPpXG552GK0t7fDw8ODX65hx+zsLFpaWlBaWsr6to1chCRJNDY2QqfTYdOmTZdcNE4oFJ5XdMxms83L0BwfH4der4ePjw+Cg4MRFBSE4OBg+Pv7O8z4rdFocMMNN4AgCBw/ftwlIsw0vGheZb75zW/C398ft956K7RaLe6++262TToPOoqsVCoZgazT6ZgfcUxMDHJzc+Hn57fmP+KkpCQIhUJGOLNd4IlrCIVClJaW4uzZs2hubmY9JZkrhIeHIzExEfX19di6dSurabm33HILPvrzn/Hdz4+iMl6CUJ/FvfgURWF4To2q8QlUj02gSjaFrukZUBSF0OBgVGzciCe/dQ8qKipQWlq64rVBSUlJGBoawtTUFKKiolbzrTkMYWFhGBwcRFZWFtum8KwzCoUC3t7e8Pb2ZtsUVqCjzKmpqS4fZR4fH8fU1BS2bdvGO5i/RKVSoba2Frm5uYiIiGDbHM5BkiTq6+thNBqxcePGVa+y7ubmhpCQkHlBIYvFwgSoJiYm0NnZCYFAwKRz02Kai9Ho2dlZXHPNNQgLC8Mnn3zicv0uL5rXgOuvvx7/+c9/sGvXLqjVajzyyCOs2kMQBObm5qBQKBihTFEU8wPNzs6+5DTr1SAhIQECgQA1NTUoKyvjo0ULEIvFqKiowJkzZ9De3o7c3FxeOAPIzMyEQqFAR0cHq1stndu7+U1kZ2Xi4c+P40/fuA7AuVTr9ulZnB2TnWvSKUyqNQCArIx0VFx/Ax7fvBkbN25EamrqJX+nbm5uSEtLQ09PDyIjI13y2ggPD0dTUxPMZjO/tYyLIZfLXXrMmJiYgNlsRmJiItumsIpOp0NbWxuKi4v59OMv0Wq1qK6uRnp6OuLj49k2h3MQBIH6+nqYTCZs3Lhx3USqWCxGREQE48SgKAoajYaZn8tkMuj1evj6+jKCOyQkhHWBKpPJcNVVVyEnJwd/+tOfOCnq1xpeNK8RW7duxdGjR3HNNddApVLhmWeeWbfJrNVqhVKphEKhgEKhgEqlgru7O0JCQhAREYHMzEz4+/tzyhMbHx8PoVCIuro6lJaW8h7RBdCVHE+fPg2xWIyMjAy2TWIdoVCIkpISnDhxAqGhoYiJiWHNlqioKPzf629g9+7dMFqtsJEUqqSTUBuNELu7o6S4GHfe8zVs2bIFGzduXPU1/PHx8RgYGIBMJkNsbOyqPrcjQFfrn5mZccn378rI5XLk5Kxf9XouQZIkenp6kJ6e7tJF8GjxEx8f7/J7ddMYDAZUVVUhPj4eKSkpbJvDOQiCQF1dHaxW64rWMK8lAoGASeumnV9ms5kR0UNDQ2huboanp+c8Ee3r67tuumJgYABXXXUVduzYgbfffttl+xteNK8hJSUlOHXqFK666irMzc3h17/+9ZoIVYvFAoVCgdnZWSgUCqjVanh7eyMkJARxcXEoLCx0iDL4EokEAoEA9fX1KCkp4Qe/Bfj4+GDjxo04c+YM3N3dkZyczLZJrEPvwdnU1AR/f39W1zd/85vfxCd//Su++OILbNxYgcf37MOWLVtQVla25pEPkUiEjIwMdHd3IyoqyiUHtLCwMMjlcl40uxB6vR4Gg8Fl62GMjY2BoiiXLoIGAG1tbRCJRPzyjC8xm82oqqpCZGQkMjMz2TaHc9C7txAEwdkK6x4eHoiKimKWXNlstnmR6Pb29nmp36GhoWu2Lrq9vR07d+7E7t278dJLL3FeS6wlvGheY7KysnDmzBlcddVVUKvV+N3vfnfZVZBtNhsjkmdmZqBWq+Hr64vQ0FCkpKQgJCTEYdOTYmNjIRQK0dDQgMLCQlajh1zE398fGzZsQHV1Ndzd3V1+sgSci/ImJiairq6O1a00BAIB3v/Tn+Du7s6KDRKJBIODgxgeHnbJyEJ4eDgaGxtBUZRLD+quhFwuR3BwMCcnvWuNzWZDT08P8vLyOJU1tt6MjIzw65jtsFqtqK6uRmBgIPLy8vi+cAFWqxW1tbUAgA0bNjhM3+Hm5obw8HBmKQq9BaxCoYBcLkd3dzdEIhFCQ0MRGhqKsLCwVQmW1dTU4LrrrsNjjz2GH/7why5/PfGieR1ITEzE6dOnsXPnTtx888346KOPLmrdHb0meWZmBrOzs5ibm4OXlxcjkkNDQ51qH9/o6GiIRCLU19fDbDYzVbZ5zhEcHIyysjLU1tbCzc0N0dHRbJvEOpmZmVCpVGhubkZpaSlrHTuba44EAgGys7PR0NCAuLg4l1tvFBISApvNBo1Gw1eIdRFceT3zwMAAfHx8XLb4HwDMzc2ho6MD5eXlDhsoWE1sNhtqamrg4eHBFw1dBJPJhOrqanh5eaGkpMSht/EUiUTM3tGpqakgSRIqlQozMzOYmJhAR0cHxGIxI6BDQ0Mven5y9OhRfP3rX8cvfvELPPDAA2v0ThwLAUVRFNtGuApKpRLXXXcd/Pz88Pe//x2+vr6LPo6iKOh0OsjlcsjlcszOzsLd3Z258ENDQ1dcWdeRUSqVqKmpQVJSEtLT0/kBYAFTU1NoaGhAUVERL5xxLiXt5MmTSExMRGpqKtvmsEZ1dTX8/Pxccp1nbW0tgoKCkJZ2cVt/OTpWqxWfffYZrrvuOoeJnFwuBEHg888/x5YtW1zOSWI0GnH06NE1qY/gKND9fVJSkktm1iyEFswCgQDl5eUOLQjXAp1Oh+rqaoSEhKCgoMDpsxIIgoBSqWQyUlUqFby9vZlodWho6LLXyD//+U/s3r0bb7zxBvbs2bOOlnMb/le1jgQHB+PIkSP42te+hq1bt+Jf//oXk35stVoxMzPDCGWz2YzQ0FCEh4cjOzt7XbZ/4hrBwcHYvHkzqqurYTab+VSjBURGRqK4uBiNjY0QCAQuHXEAzq0BorfnCgwMRFhYGNsmsUJWVhZOnTqFxMREl3Cu2RMZGYmxsTGXE82uiEKhgLu7O/z9/dk2Zd3p6elBRESEywpmkiTR0NCAoKAgvrYHeMF8IVQqFaqrqxEXF4esrCyXmEeKRCKEhYUhLCwMmZmZsFqtTCp3R0cHUwuCFtH0emiKovD666/jBz/4Af70pz/ha1/7GttvhVPwkWYWsFgs2L9/Pz777DO89tpriIiIwNzcHHx9fZkLOCQkxCWL+SyGwWBgomfFxcX857KAyclJNDY2ori42OWFMwCMjo6iq6sLlZWVLicaaZqbm2G1WlFWVsa2KeuK0WjE4cOHcc0116xLejpFUUwjSfKC5wvbwuewv2+540IIgkBzczMKCwvP6x/tJ4j0uf1x4fnCZv83oVA477jU+XrQ3t4OgiBQUFCwLq/HFdRqNU6fPo3t27e7bP/W0dEBuVyOyspKlxeINpuNWaPLC+bzmZmZQV1dHdLS0lw6A20her2eCdLNzMzAzc0NQUFBeOONN3Dw4EH84x//QEVFBdtmcg5eNLMERVF45ZVX8PTTT+PFF1/E3Xffza/JWQaz2Yza2lqIRCKUlZW5TAriSpmYmEBTUxNfdfxL2traMDs7iy1btrjktWIymXD06FGUlZU5bcSdFqQEQcBms4EgCBAEgYaGBkRHRyM4OJi5j36c/TlJkuedL3abFr1L3bccC0XlxYhU++NS99l/FiqVCoGBgef9fTHBvVCw25+vtC33nun3bd8Wu08oFEIkEi16bn9bJBIxjb7d0tKCpKQkREREzPs7/RhnhKIonD17FkFBQcjOzmbbHFYYHR1FZ2cnKisrl1zi5irQgpmiKGzYsIEXzAug50V5eXl80dRlIEkSQ0ND2LNnD7RaLf7973/z+3ovAS+aWeY///kPbr/9djz66KN4+umnXSJt5FKx2WxMcTC+8Mf58ML5v5AkiZqaGgiFQpSXl7vk72pgYABjY2OcqCpLURRsNhvTrFbrvNu08F14bn+bFr32t+2hBRMtaH18fJYUXSsVa8sJPvv7Fou4AosL3LVgvdc0LxdRX4mjYTEHxVLOi4VOD4IgYLVaYTAY4OHhwdxnP5VZ+J27ubnNO6fbUrfd3d3n3U//jW2kUik6OjqwY8cOl3QGKhQKVFdXo7y83GmdgSuFF8zLMzQ0hK6uLn4+tAL6+vpwww03ICMjAx988IHLO6OWgxfNHKCjowM33HADysvL8Yc//IEXg8tAkiRaWlowMzOD8vJyBAYGsm0Sp6CFM18c7NwyiFOnTiEqKsolozIkSeL48eNITEy8rAr0tOC1Wq2LNvu/LTynb9sL3IVCxd3dfd59y50vJYREIhEjUOfm5lBVVYVrr72WdWfBeuFqhcD6+/uhUCiwYcMG5r7Fsg4W3l7OKUOfL3To0FMkoVB43nVLHxeeL7xPLBbD3d39sq5Hm82Go0ePIisrCxKJ5LI/Q0dDr9fj1KlTyMjIQGJiItvmsIrVakVdXR0vmBeBJEl0dHRAJpOhvLzcZdf9r5SjR4/i5ptvxne+8x384he/cJkx81Lhf2kcICcnB7W1tbjxxhuxdetW/POf/+TXpi6BUChEYWEh+vv7cfbsWRQXF/NeRDuio6OZfa4JgnDJyRWNWCzGhg0bcOrUKfj5+blcepZQKEROTg4aGxsRExMDsVgMq9UKi8XCtMVu0/fZC2NaOCwlCtzd3eHp6Xne3xYKDDc3tzWPvgYGBkIkEkGhULh8NMpZmZqaOq9vo6P+q+k0oCPji2VHLHQUWSwWGAyGRR1LdBq/SCRiBDTd6NtisZhpC2+LRCL09/fDy8sLsbGxq/b+HAV6b92YmBiXF8wWiwXV1dVwd3fn1zAvwGq1orGxEQaDwaVrmqyUt956C4899hjeeOMN7N27l21zHAL+18YRwsPDcfToUdxzzz0oLS3Fv/71LxQVFbFtFicRCARIS0uDr68vGhoakJGRgeTkZJdMwV2MyMhIlJeXo66uDjabzaUnGb6+vigtLUVtbS18fHwQEhLCtkmrhs1mg8VigdlsnndceE6SJA4dOsRM3GlhYT8ppyfq3t7ei07o6eYIvzGBQIDIyEhMTk7yotkJMZvNmJubQ0lJyZq/lkAgYDIZPDw8Lvl56Aj2QmeU/W2tVnueM8tqtQI495slSRI+Pj6orq6GWCyGh4cH89td7NwRfqsrgaIoNDY2wtPT0yW30bOH3mfYx8eHL4q6AIPBgNraWnh4eLhsLZOVYrPZ8Mgjj+D//b//h0OHDmHTpk1sm+Qw8KKZQ3h4eOAPf/gDXnrpJWzduhV//OMfcdNNN7FtFmeJjo6Gl5cXamtrodfrkZuby6eWfElYWBgqKipQU1MDm83m0lUjw8LCmGyOLVu2wM/Pj22TFoVOgzabzTCZTDCbzcs2OuV5sQmzp6cn/P394eHhAZIk0draitLSUoSHh89LZXZWoqKi0NraitzcXKd/r67G1NQUAgICHGoZEy28PT09L+r/SJKExWJBfX09xGIx4uLi5jnF9Ho9lErlPGeZfb+wsNF9hIeHBzw9PZlzro6bFEWhra0Ner0elZWVnLVzPTAYDKiqqkJQUBAKCwtd+rNYyNzcHGpraxEVFcXPAy+ASqXCrbfeiomJCdTV1SEhIYFtkxwKXjRzDIFAgO9973vIyMjAHXfcgZ6eHvzwhz/kJ35LEBQUhMrKStTW1qKmpgalpaW8h/FLgoODsWnTJlRVVcFqtSIzM9Nlr6OEhAQYDAbU1NRgy5YtFz15vRwoioLVaoXRaITJZGKavTimjwRBQCgUnjexFYvF8PPzW3QSvJLv1GQyobe3F5GRkS5xDYSGhsJisUCj0SAgIIBtc3hWkampKZdZkiMUCqFQKKDX61FeXr6ibdTsM1AWNo1GM++2xWIBgPP6G/ujp6cnvLy84OHhse6RzYGBAUxOTqKystKlx3WdToeqqipEREQgLy/PJfrwlULXccnMzERSUhL/2SzDwMAAbrjhBiQnJ+Ps2bMuucf95eLyovmZZ57BRx99hPHxcYjFYhQXF+PnP/85ysvLmccolUo8+OCD+PTTTyEUCnHTTTfhf//3f+dVmHv33Xfxs5/9DMHBwXjrrbfm/f+lsGvXLpw5cwY33HADuru78e677zqUZ3098fb2xubNm9HY2IhTp06hvLycr/73JQEBAdi8eTOqqqpgs9lcOvKWmZkJo9GImpoabN68eVXWgpEkCZPJBKPRyDR7YUw3kiTh5ubGTELpFhwcfN5EdS3SoFNSUjA+Po6hoSGkpKSs6nNzEZFIhIiICExMTPCi2Ymw2WyQy+XIyspi25R1wWq1or29HdnZ2Sved5yuHeDt7X3BxxIEAYvFMs+RRzvxtFrtvPspimKyWOybl5cXc/Ty8lq1/mt8fBx9fX3YtGnTit6Ls6JWq1FdXQ2JRIKsrCyXHb8XQlEU+vr6MDAwwFfIXgHHjx/HN77xDdx999345S9/yaf2XyIuXz37ww8/RHh4OJKSkmA0GvHqq6/i448/xsDAALMe7tprr8Xk5CTefvttWK1W7Nu3D6Wlpfjwww8BAGNjY9ixYwfee+89yGQyPP300+jq6loV+6anp3HTTTdBo9HgL3/5CzIyMlbleZ0RkiTR1dWFsbExFBcXIyIigm2TOAOd2hUQEICioiKX7TBJkkR1dTWzFdVyaVwURc0TxAaDgRHF9H1msxkCgWDRCeTCySXbBVtmZ2dRU1ODHTt2uIQDTiaTobe3F1dccQXbpqw5rlI9WyaToaenBzt27GDblHWhvb0dGo0GGzduZFUsURTFiGm62TsI6f7QZrMxaei0iLZv3t7e8PLyumBfODMzg9raWpSVlSE8PHyd3iX3mJmZQV1dHVJTU5GamsoL5i+xWq1obm6GSqVCeXk57xhdBpIk8fzzz+MXv/gFXnvtNdx9991sm+TQuLxoXgidznfkyBHs2LED3d3dyMrKQn19PVN45ODBg7juuusglUoRHR2Njo4O7Nu3DydOnIBcLscVV1yB4eHhVbPJarXixz/+Md544w289dZb2L1796o9tzMyPj6O1tZWpKamIi0tjR9ovsRsNqO2thZCoRBlZWUrjlw4G1arFWfOnEFAQADS0tLmCWL6SDeSJOHh4cFM9uhmPynk8prAhTQ1NcFms6GsrIxtU9Ycq9WKgwcPYtu2bZxdx75auIporq+vh6+vLzIzM9k2Zc1RqVQ4c+YMtm7d6jDXr/0yFPt+1L4RBAGxWDxPRNsfCYJATU0NcnNzXW7HA3ukUilaWlqQl5fn0p/DQnQ6Herq6uDh4YGSkpLLKtDn7Mjlctxxxx0YHh7GX/7yFxQWFrJtksPj8unZ9lgsFrzzzjsICAhAfn4+AKC6uhqBgYHzKnVeeeWVEAqFqK2txde//nXk5OQgLy8PAQEBEIvFePfdd1fVLnd3d7zwwgvYunUr9uzZgxMnTuA3v/mNS0SLLgWJRAJ/f3/U1dVBpVKhqKjIqSeSK8XDwwMbN25EY2Mjzpw5g4qKCqe+hujoiMFggF6vh8FgYJrFYsH4+DjGx8fh7e09b9IWEhIyTyQ7U1Q+Ozsbx44dw8TEhNPv4+3u7o7w8HBMTk46jOjgWRqCIDA9PY20tDS2TVlzSJJES0sLUlJSHOrapavsL7VWkqIoZmsu+wwehULB9NM2mw1CoRBDQ0OYmppi+mcfHx/m3Jn65IVQFIXBwUH09vaitLSUz5izY2pqCo2NjUhISEBmZqbDOKvZ4OTJk/jmN7+JyspK/PWvf+XXL68SvGgG8O9//xu33XYbDAYDoqKicPjwYYSGhgI49yNdmB7k5uaG4OBgTE1NMff97ne/w4svvshMtteC6667Ds3NzbjttttQXl7Op2svQ0BAALZu3YqGhgacOnUKZWVlDjX5WCvc3NxQWlqKtrY2nDp1ChUVFQ7dmVIUBaPRCL1ev2gjCAIeHh7zJlzBwcHw9vYGRVFoaGhAYmKiS6zzBc45TnJzc9HW1obQ0FCnzzaIiorC0NCQSwgtZ0cul8PDw8Oh+6uVMjAwAJIkne66FQgETNGxoKCgeX8zmUw4c+YMYmJimMKNdJPL5cw5QRDw9PSEr68vI6btmyM7yCmKQkdHB2QyGTZt2oTAwEC2TeIEFEWht7cXAwMDKCwsRExMDNsmcRaSJPGLX/wCzz//PH71q1/hnnvu4bMtVxGXEs0ffPAB7rnnHub2559/ji1btmD79u1oaWnB7Ows3n33Xdxyyy2ora296LU067EHrEQiwYkTJ/DjH/8YpaWlfLr2MojFYlRUVKC7uxunTp1CUVERoqKi2DaLdYRCIfLz89HX14fTp0+jrKyM0/vZ0muL9Xo9dDoddDodc67X6wEAXl5ezKQpODgYEomEEcrLrZ/buHEjzp49C3d3d8THx6/XW2KVmJgYSKVSdHR0OP1e8PTWUzqdji8O6ODQ2RHOPgHUarVMASxXiaRZLBZUV1cjKCgI+fn5EAgEiwpGeiwwGAzQ6XQwGAzQaDSYnJyEXq+H1WqFWCyGr68vfHx84OvryzQfHx9OR6gJgkBTUxPUajW2bNkCHx8ftk3iBFarFY2NjdDpdKisrHQJp9mlYp+OfebMGT4dew1wKdG8a9eueVWtaW+Vj48PUlJSkJKSgg0bNiA1NRW/+93v8OSTTyIyMhJyuXze89hsNiiVStaq9fHp2itHIBAgKysLgYGBaGpqQlJSEjIyMpx+4nUhBAIB0tPT4enpidraWuTn50MikbBqk81mg16vh1arhVarnSeSCYJgogq+vr4IDQ1FQkICI4wvdXIZGBiI8vJy1NTUwN3d3elTloFz331+fj6OHTsGuVzu1IV26BRtmUyG9PR0ts3huUQIgsDk5CQ2b97MtilrCkVRaG5uRkJCwnmRWGfFZrOhpqYG3t7eKCwsXHZsFggEzLKZxYIU9N7VtENVo9FgYmKCGUO8vLwYAe3n5wdfX1/4+fnB09OT1TmB2WxGfX09SJLEli1b+HW6X6LRaFBXVwdfX19s3brVobMI1poTJ07g9ttv59Ox1xiXEs1+fn4rStElSRJmsxkAUFFRAZVKhcbGRhQXFwMAjh07BpIkL3tbqcuFT9deOdHR0fD19UV9fT0UCgWKi4t5JwOA+Ph4eHl5ob6+Hlqtdl32crZYLIww1ul0zNFgMMDNzY2ZzPj7+zPf21pGCUJDQ1FSUoKGhga4ubk5tYik8fLyQnZ2NlpaWrB9+3annoxER0djYGCAF80OzPT0NDw9PZ2+Su7Q0BDMZrPLjOMEQaCurg4ikQglJSWXHVkXi8UQi8XnORzo+hb2WUpyuRxDQ0PQ6/UQiUTzRDR99PHxWfNov0ajQW1tLbOzBdu7LHABiqIwOjqKjo4OpKSkID093eUDHUtBEASef/55Ph17nXDp6tl6vR4///nPsWvXLkRFRWF2dhavv/46PvzwQzQ2NiI7OxvAuS2npqen8dZbbzFbTpWUlDBbTrGN1WrFU089hddff51P174ANpsNbW1tmJ6eRlFREV9k40u0Wi1qampWdeC2Wq2MONZoNMzRbDYza9LsJyh+fn7w8PBgrcOnq65XVFSsy1ILtqEoClVVVfDx8UFBQQHb5qwZdBXtrVu3Oq333dmrZ7tC1WydTocTJ06gvLyc08tlVguSJNHQ0ACj0YiNGzeydt0SBMFkNC105AJgHLj0GOXv7w9vb+9VGaemp6fR0NDAZ8DZYbVa0draitnZWRQXF7vEb+FSsU/H/vjjj516HOcKLi2aTSYTbr/9dtTW1mJ2dhYhISEoLS1l1gvTKJVKPPDAA/j0008hFApx00034Te/+Q3n1sh99tln2LNnD772ta/hN7/5Dby9vdk2ibOMjY2hra0NiYmJfBXGL7FYLKirq4PNZkN5efmKI/EkSUKn00Gj0UCtVjMC2Wg0wsPDY95kgz5ydWI/MjKCzs5OVFRUIDg4mG1z1hyDwYDjx4+jpKTEqR1IdXV18Pf3d9oInjOLZpvNhoMHDzr1ekaSJHHmzBkEBQUhNzeXbXPWHJIk0dTUBK1Wi40bN3IyHZmiKCbF2975q9PpIBQKmXEtICAA/v7+zO4pK33u4eFhdHV1cWJpFFdQqVRoaGiAt7c3ioqK4OnpybZJnMU+Hfudd95x2r6Ra7i0aHZGxsfHcdttt0GtVuPAgQNMSjnP+Wi1WjQ0NDCpYbyT4dxkprW1FdPT0ygvLz8vzc1isTDC2F4gCwQC+Pv7z2t05NjRGBoaQnd3NzZu3OgS6wpHR0fR3d2NK664wmmraU9MTKCrqws7duxwymiOM4vm8fFx9Pf344orrmDblDWjr68P4+Pj2LZtG6eLVa0GFEUxBa82bdrkcGME7SSmRTTdDAYDPD09mfGPFtO+vr7znPIkSaK9vR2Tk5MoKytzCefshbB3IqSlpSE1NdUp++nVwGw247nnnsOrr77Kp2OzAC+anRCr1Yrnn38ev/zlL/G9730PP/rRj5xuIrVaEATBbPFQWFjIV9fGf/eJ7O7uRlJSEtzc3KBSqaBWq2E0GuHt7X3exMDHx8epOu7BwUH09PS4hHCmKAq1tbVwc3Obtx+9M0EQBA4ePOi0GQTOLJqrq6sRGhqK1NRUtk1ZE9RqNU6fPo1Nmza5RF/T1NQElUqFTZs2OVUk0Wq1znMm042iKAQEBCAgIAC+vr6QSqUgCAIbNmzgHfU454hvbm6GSqVCSUmJSyyNulRaWlqwd+9eCIVCHDhwAHl5eWyb5HLwotmJaW5uxt69e+Hm5oYDBw64RNrXpSKTydDS0gKJRILs7Gyn9/YvxGg0QqVSMU2tVjPF8Pz8/BAXF8cM/M4ajVzI4OAgent7sXHjRqffL9NkMuH48ePIy8tz2j0wm5ubIRKJnHKi4ayi2WQy4dChQ7jyyiudUmAQBIFTp04hMjLSqddrA/+tDD43N+d0gnkpKIqCTqeDWq3G9PQ0JiYmQE+5aadzQEAAgoKC4O/v73LzDqVSiYaGBvj7+6OoqMhl5hYXi9VqxS9/+Us8//zzeOKJJ/CjH/2I/6xYgi/T58QUFhaivr4eP/3pT1FeXo6nnnoKTzzxBF+dcRFiYmIQGBiIxsZGnDx5EkVFRU4rlCwWC1QqFebm5pij2WyGn58fAgMDERERgfT0dPj7+8NoNKKurg5yuRxxcXEu1VEnJyeDJElUVVVh06ZNTl2519PTE/n5+WhpaUFwcLBTVpaPjY1FQ0MDcnJy+BoGDoJMJkNwcLBTCmYA6OnpYbb/c2YoikJrayuUSqXLCGbg3BZZfn5+UKvVmJycZFKPjUYj1Go1VCoVpqam0NPTA5vNhoCAAAQGBiIoKAiBgYHw8/NzqgwuGpIk0dPTg6GhIWRmZiIpKckp3+dq0NnZib1798JkMuH06dP8kkuW4SPNLkJdXR327t0Lf39/HDhwwGkL4lwuJEmiv78f/f39TrG2hiRJqNVqzM3NMU2v18Pb25sZmIOCghAQELCkM8VqtTLpU2VlZU7rTFgK+nqoqKhw+vTJ5uZm6PV6bNq0yaGv+8WgKAqHDh1CQUGB0xU9c9ZI88mTJxEfH4+EhAS2TVl15HI56urqsHXr1hVthemokCSJlpYWRjA7o0NuKUiSRFdXF8bGxlBcXLxkv0NRFAwGwzxHtlqtBgBmjA4ODkZQUJDDOxw0Gg2amppAURSKi4v5AlZLQBAEfvWrX+HZZ5/Fgw8+iGeffdbh1v87I7xodiGMRiOeeuopvPnmm3juuefw0EMPuVw60EqZm5tDY2MjPDw8UFRUBB8fH7ZNWhFmsxlKpRJKpZIZgAUCATPg0kL5YjtfiqLQ39+Pvr4+5OXlIS4ubo3eATeh1zhv2LDBqddc2Ww2nDhxAnFxcUhLS2PbnFWns7MTRqPR6dZuO6No1mq1OHHiBK6++mqny3Axm804fvw4MjMzER8fz7Y5awZJkmhsbGSqZDu64LsYTCYTGhoaYLVaUVpaetG7rdAFx2hnt1KphFarhbe3NzOeBwcHw9/f3yEyZyiKYopsJiYmIiMjg59/LkFfXx/27t0LpVKJAwcOYMOGDWybxPMlvGh2Qc6cOYO77roLUVFR+MMf/oCUlBS2TeIkNpsNXV1dGB8fR05ODuLi4jgVfaO3xFAoFFAoFFAqldDr9fDz85vnmV7NFC+5XI6GhgbExsa6XJrryMgIOjo6nH4fVZVKhTNnzmDjxo1OVzRLo9Hg5MmTuOaaa5xGXALOKZqd1cFBF94TCoUoLS3l1JiymhAEgYaGBhgMBs5uK7VWKJVK1NfXIyQkBAUFBau2JM5qtTICmnaMUxTFjPchISEIDg7m3BI8o9GI5uZm6HQ6FBcXO7Xj+XIgSRK/+c1v8OMf/xjf+c538LOf/cxpl6Y4KrxodlH0ej1+8IMf4A9/+AN++ctf4r777nMpAXQxTE9Po7m5GUFBQSgoKGBt8KcoCmq1ep5Itlqt5w2Yaz1pNhgMqKurY7bqcqV0O3p/79LSUqdL8bVnYGAAw8PD2LZtm9OIMBpnTPl1NtHszKn0Q0NDGBgYwLZt25wugk5js9lQV1cHq9WKiooKp32fC6EoCiMjI+js7FyXtboURUGr1UKpVDLzApPJhICAAISEhDBzAjYdFlKpFG1tbYiMjERubq5T9E9rwdDQEPbt2wepVIo//OEPqKysZNsknkXgRbOLc/z4cezbtw/Jycn43e9+51QTydXEYrEw67Ly8vIQHR295q9Ji+TZ2VnMzs5CoVAAACOQQ0JCEBgYyEqKE0EQaG9vx8TEBIqKihAZGbnuNrCFTCZDc3MzioqK1uU6YAOKolBdXQ2xWIzi4mKnioYNDw9jfHzcqSYlziaap6en0dLSgp07dzrVtUdvL7VhwwaEhoaybc6aYLVaUVtbCwAoLy93iutxJVitVmaOUFxczNr3azAYGKe6QqGAVquFn58fQkNDmbYeTgyz2Yz29nbMzMwgPz/facfKy4WiKLz11lv43ve+hz179uCFF1646FR+nvWDWzkcPOvO9u3b0d7ejscffxx5eXl4/vnnce+99/JrTRYgFotRWloKqVSKlpYWyGQy5ObmruoaLdprPDs7i5mZGSgUClAUhZCQEISGhiIjIwMBAQGcmESKRCIUFBQgNDQUjY2NiI+PR1ZWlktkK8TExEAkEjHr1ZxxTaJAIEBRURFOnDiB0dFRp3KmxcTEoKOjg5lM8nCP8fFxxMbGcqKvWy2sVivq6+uRmprqtILZbDajpqYG7u7uKCsr41ya8FoxNzeHhoYG+Pr6Ytu2baxGdr29veHt7Q2JRALg3HeiUCgwOzuLnp4eaLVaBAQEMAI6JCRkVR0bFEVhYmICbW1tCA4Oxvbt211qLfvF0N/fj/vuuw99fX34+9//jiuvvJJtk3guAB9p5mE4dOgQ7rvvPgQGBuKtt95yurVkq4XJZGI8qLm5uZc1uTMajZiZmWGazWZDcHAwQkNDERYWhoCAAM4LUZ1Oh/r6eiZd21XW4MzMzKCurg5paWlISUlxqgk+zezsLGpqarB582anqppeX18Pb29vZGdns23KquBMkWaLxYIvvvgC27ZtcxqnBkVRjJOtoqLCKfsKg8GA6upqZs9dV3C82xe3Sk9Pd4hxwGQyQaFQYGZmBrOzszAYDAgMDERYWBjCw8MRFBR0yXMOo9GItra2eRl5XP882MBkMuGXv/wlXnzxRezbtw/PP/88X0XcQeBFM888TCYTXnjhBbzwwgvYt28ffvaznzn9NjuXCu1NDQwMRH5+/orW9hIEAYVCAblcjpmZGWg0GgQFBa3KgMUmBEGgo6MDMpkMhYWFiIqKYtukdUGlUqGmpgaxsbHIzs52yglCb28vxsfHsXXrVocXZDRyuRxNTU3YuXOnQ/7eFuJMonlwcBATExPYsmUL26asGsPDw+jr62M9CrlWaDQaVFdXIzIyEnl5eU7ZDy7EYrEwWzGWlJQ4bHEre8e9XC4HSZIIDQ1FeHg4wsPDV7RzCEVRGBsbQ2dnJyIiIpCTk+OU1/lqcPDgQTzwwAMICgrCm2++yQenHAxeNPMsSn9/Px588EE0Nzfj5Zdfxh133OESA+HFYrFY0NHRgcnJSWRnZyM+Pv68z0mv12NqagrT09NQKBTw8PBAeHg4wsLCEBYW5lRFUmQyGVpaWhAXF4esrCyXiDbodDpUV1cjODgYhYWFTiHC7KEoCjU1NXBzc0NJSYlT9AMUReHIkSPIzs52irV2ziKaKYrC8ePHkZKS4jTb2tHV6CsqKhxWWC2HUqlETU0NkpKSkJ6e7hT9w4VQKpVoaGhgourOMobTdVRop75CoYC3tzfCw8MRERGB0NDQ88Z0g8GAlpYWaLVa5Ofnu1R9k4tBKpXi4YcfxtGjR/GLX/wC3/nOd1xifuRs8KKZZ0koisInn3yChx9+GCkpKXjjjTeQlZXFtlmcRC6Xo6WlBT4+PsjLy4PZbGaEssFgQEhICCIiIhAeHg5fX1+nnljo9Xo0NDSAJEkUFxe7RNqRyWRCdXU1vLy8UFJS4nRr+cxmM06cOIGUlBQkJyezbc6q0NvbC6VSiYqKCrZNuWycRTQrlUpUV1fj6quvdorfkMViwcmTJ5GQkIDU1FS2zVl1pqam0NDQgOzsbCQmJrJtzppDkiT6+/vR39+PjIwMJCcnO/VYbrPZMDs7i+npaUxPT8NisSAsLIyZy0xNTaGrq4vJtHLkvmetsFqt+M1vfoNnnnkGX//61/HSSy853Y4ArgQvmnkuiFarxbPPPos33ngD3/3ud/HUU0+tKGXHlbBYLJiYmMDAwAD0ej1EIhGio6MRGRmJsLAwlxtMSJJEb28vBgcH12XrDS5AV40lSRLl5eVOl56mVCpRVVXlNJV/jUYjDh8+jCuvvNLh1+E7i2hubm6GUChEfn4+26ZcNnSGhlAoRFlZmdP1f6Ojo2hvb0dhYSFiYmLYNmfN0ev1aGxshM1mQ3FxMQICAtg2aV2hC5VOT09DJpNBrVZDIBAgJiYGSUlJCAwMdLpr/HI5c+YM9u/fD5Ik8cYbb2Dr1q1sm8RzmThXHiHPmuDn54eXX34ZNTU1OH36NLKysvCPf/wDru5vMRqNGB4eRlVVFQ4ePIiRkRHExsaioKAA3t7emJubg1gsduhJ7KUiFAqRmZmJiooKDA0Nobq6GkajkW2z1hR3d3dUVFTAy8sLp06dglarZdukVSU4OBg5OTloaGhwiu/Sy8sLERERGBsbY9sUHpwT/jKZzGmq0Xd3d8NgMKCoqMipxARFUeju7kZnZyfKy8udXjBTFIXR0VEcP34cgYGB2Lp1q8sJZuDcjgpeXl4wm83Q6XRISkpCfn4+KIpCVVUVDh06hLa2NszMzIAkSbbNZZWZmRncfffduOaaa3DnnXeiubmZF8xOAh9p5rkoSJLEgQMH8MQTT2DDhg147bXXXCIti0an02FychKTk5NQqVQIDg5GVFQUoqKi5kWrSJLE0NAQenp6EBUVhezsbJfddsFqtaK9vR1TU1MoKChwijWky0FRFLq6ujA6OoqysjKniMrSUBSFlpYWaDQabN682eHXZE1PT6O5udnhC4I5Q6R5aGgIUqnUKfbPnpiYQHNzMyorK52mAjhwruAjvRfxhg0bnOq9LYbZbEZrayuUSiUKCwtdNq2W3kaqo6MDPj4+yM/Pn/fdkySJmZkZTE5OYmpqChRFITIyElFRUQgLC3P4cWKlkCSJ3/72t/jBD36Abdu24de//rXT1GbgOQcvmnkuCYVCgSeffBIffPABfvjDH+Lxxx93unRUGr1eD5lMBplMBp1Oh9DQUCb1+kLv2Wg0or29HbOzs8jMzERCQoJTRR0uBplMhtbWVkRGRiI3N9dhJ/crZWRkBB0dHSgoKEBsbCzb5qwaBEHgzJkzCAgIQEFBAdvmXBbOUhDM0UWzMxUA02g0OH36NAoLCx36mlqIxWJBXV0dCIJAeXm50zuB6Qr7QUFBKCgocNr5zYXQ6XRob2+HSqVCTk7OBbfYpCgKSqWSCS6YzWZERUUhJiYG4eHhDu2cXI7m5mbs378f09PTeO211/CVr3yFbZN41gBeNPNcFjU1Ndi/fz/0ej1eeukl7Nq1yylEodFoxMTEBKRSKdRqNcLDwxETE4PIyMhLmpROT0+jra0NYrEY+fn5TrXn7cVgNBrR1NQEg8GAgoIChIWFsW3SmjI9PY2GhgakpKQgLS3NKX4bwLnv8cSJE4wjyJHp7++HXC7Hpk2b2DblknF00Tw7O4u6ujpcffXVDh2VslqtOHnyJKKjo52qaKZer0dNTQ18fX1RXFzsFEXalsJqtaKrqwvj4+PIzc1FXFyc0/TbFwNBEBgYGEB/fz8kEgkyMzMvuko4XY2bDjrYbDZGQIeGhjqFgJ6ensazzz6LP/7xj3jsscfw5JNPOnyNDJ6l4UUzz2Vjs9nw7rvv4plnnkF6ejpefvlllJWVsW3WRWOxWJjOXalUIiQkBLGxsYiKilqVLSUIgkBfXx8GBwcRFxeHzMxMh5zgXi4URWF4eBhdXV2QSCTIyspy6s9BrVajpqYG4eHhyMvLc2hRYM/s7CxqampQXl7u0M4Ps9mMQ4cOYdu2bQ6bburoormhoQGenp7Iyclh25RLhiRJ1NbWQiAQoLy83GmEllKpRG1tLWJjY5GTk+M072sxZmZm0NzcDB8fHxQUFLhswVO5XI62tja4ubkhPz8fQUFBl/2cFEVhbm6OmWNRFIXo6GhIJBIEBQU53HVlMBjw6quv4oUXXsCOHTvwwgsvIC0tjW2zeNYYXjTzrBparRYvvvgiXnnlFezatQu/+MUvOL/emSRJyOVyjI+PY2pqCgEBAYiNjUV0dPSapZ9ptVq0t7dDrVYjIyPDZVO29Xo9WlpaoNfrUVhY6NDC60IYjUbU1tZCJBKhrKzMaVL9RkdH0dnZicrKSvj6+rJtziXT2NgIsViM3Nxctk25JBxZNJtMJhw+fBjbt2936Guovb0dMzMz2LJli8N9B0tBV8jOyspCUlIS2+asGfbR5ezsbJcdk3U6HTo7OzE7O4uMjIw12/WCoigoFApIpVLIZDJ4eHhAIpFAIpFwPkpLEATee+89PPXUU4iJicHLL7+MLVu2sG0WzzrBi2aeVUcqleLpp5/Gn//8Z9x///340Y9+tCqeytVErVZjfHwcUqkUAoGA6bDXK9JEURSmpqbQ2dkJkUiEnJwcpxaNS0FRFEZGRtDZ2en0UWebzcYU0SkvL3eaCqwdHR2Ynp5GZWWlw3539P7AO3fuvOj3QFEUSJIEQRAgCII5J0mSOacfs7DR91MUNa/Z30e/Bn1ceB9wzvk3NjaGuLg4JuVRIBAwE97Fzu2bUCg87/bCJhAIIBKJmNv0uf3R/nVWSk9PD+bm5hx6v+yRkRF0d3ejsrLSKaKTJEmis7MT4+PjKC0tdeqxSS6Xo6WlxaWjy1arFX19fRgaGkJcXBwyMjLWzbFLEASmpqYwPj4OuVyOkJAQSCQSREdHc24ZwKFDh/DEE09Aq9Xi+eefxy233OKSzhVXhhfNPGtGa2srvve976G+vh4//vGPcf/997MaYbNarZBKpRgdHYVOp0NUVBQkEgnCwsJY6/gIgsDQ0BD6+voQFhaG7Oxslxy0XSXqTFEU+vv70dfXh6KiIqcoFERRFGpra0FRFMrLyzm9To0kSdhsNthsNlit1nnHrq4uBAQEICAgAARBMI+z2WyMIKbvt7+9cHuVxcTlckJ0McFqf5t+Tvtz+yPw334kKSkJIpFonqBeKLTp84VifaGIX0zk27fF3jsAiEQiuLm5QSQSndfc3NyYRt83MDCAuLg4hISEwM3NDe7u7vOO9OfHVWZmZlBbW+s0+5dbLBY0NDTAZDKhvLzcaccjPrp8rj8YGxtDd3c3/Pz8kJOTw6oz12QyQSqVYmxsDAaDATExMUhISGB9D+i2tjY88cQTnJnL8rAHL5p51hy2vXNzc3MYHR2FVCqFn58f4uPjERMTw6momMlkQnd3N2QyGZKSkpCamsop+9YD+6hzbGwssrKyVmUtOReZnJxEU1MTkpOTkZ6e7vCTNavVitOnTyMsLGzNU5wpioLVaoXFYpnXrFYrcz99vrARBME8z0JxZrPZoNfrmQiHvbizF3lLCUJ7MbzesJWebS+u7R0JizXa2WDviFCr1dBoNAgKCprnwKD/DpxzDtDfE93EYvF5t+n76HOxWLzm9QN0Oh1OnTqF7Oxsp9hfWqvVora2lin45axj0PT0NFpbW+Hj44PCwkLOpwSvBbOzs+jo6IDVakV2djaioqI4Mw7RBcRGR0cxPj4OX19fJCQkrPu8TSaT4amnnuJ01iTP+sKLZp51gV4H8uMf/xixsbFrvg7EZrNBKpViZGQEOp0OsbGxjMeSy6hUKnR0dECn0yErKwsSiYQzA9l6YTAY0NraCrVajZycHMTExDjlZ6DRaFBbW4uAgAAUFhY6/ARVr9fj1KlTSEtLQ3Jy8or/jxbBJpMJZrMZZrMZFotl3tH+3Gq1AgCEQuF5Iom+7ebmdp6wshfJi0UvSZLEoUOHHHI/Vkdc00xRFE6dOgWJRLLoelk6K4B2eNhstiUdIvaOE/oInIt6u7u7w8PDAx4eHhCLxfOOC9vFiGyz2YzTp08jMjLSoQuY0dCV/hMTE5GZmemUfa7JZGLWnmdlZSE+Pt4p3+dyGAwGdHZ2Ynp6mumruVyc0mq1QiaTretcTqvV4oUXXsCrr77qMPV5eNYHXjTzrCt0xcFf/vKXuPLKK1e94qBer8fw8DBGR0fh4+PDinfycqEoChMTE+js7IRYLEZ2drbTpisvBf0ZtLe3IyAgAHl5eU6ZJmg2m5lUyLKyMoet3kyjVCpRVVWF4uJiREREwGw2w2Qyndfo+2lBTFEURCIRPD095wmahSJnYRRxtSe8vb29UCgU2Lhx46o+71rjiKL5ctaRXwiSJOeJ6cUcMfYOGbPZDADzBLaHhwc8PT0XbUKhEGfPnoWXlxdKSkocWnhRFIW+vj709/c73Z7yNBRFMUULIyIikJOT4/T7TC/EYrGgv78fw8PDzJZojvYZ2GcNBgQEIDk5GZGRkauW3eMsO8HwrB28aOZhBXpvuz/84Q/41re+haeffhrh4eGX9FwURUGpVGJwcBDT09OIjIxEcnKyQ25jYI/9eufg4GBkZWU5TfGolWKxWNDV1QWpVIr09HQkJydzes3spUCSJHp6ejA8PIzCwkKHWOdMkiRMJhOMRiNzpJtGo4Fer2ceKxaLFxUetCihBQoXir7Q209t3boV/v7+bJuzYhxRNNfV1cHb25sTUVqKouYJaHuHDn2N040kSWaNeXBwMLy8vBZtjvA9WCwWNDU1QafTobS01CnHF41Gg9bWVphMJuTl5TlcFsnlQhAEhoeH0dfXh4CAAGRnZ3M+4+5CWK1WjI6OYmhoCACQmJiI+Pj4S17ORVEUPv30U3z/+98HRVF44YUXsGvXLoeeP/KsDbxo5mGVnp4e/OAHP8CRI0dw//3344knnlhxMRWSJCGTyTA4OAiDwYD4+HgkJiY63foki8WCvr4+jIyMICoqCpmZmU73Hi+EQqFAS0sLhEIhCgoKnHJd0cTEBJqbm5GQkIDMzExWnQMkScJoNMJgMDDN/rbJZAJFUfD09GREgv25UqnE+Pg4tmzZ4nDR85aWFpAkiaKiIrZNWTGOJpr1ej2OHTuGHTt2OFRfRlEU2traMD09jezsbNhstnkOI7oRBAF3d3d4eXnB29ubOdLNx8eH9e9JrVajrq4O/v7+KCoqYt2e1YYgCPT19WFwcBCJiYlIT0/nhGNuvaAoCuPj4+jp6YFYLEZWVharRU/XApIkMTU1haGhIahUKkgkEiQnJ6946zqKovDFF1/g6aefxujoKH7yk5/gf/7nf5zut8CzevCimYcT1NfX4yc/+QlOnz6Nhx56CI8++iiCg4MXfazNZsPo6CgGBgYgEomQnJwMiUTi9AOiwWBAT08PZDIZEhISkJaW5lIVHAmCwMDAAPr7+xEXF4fMzEynG9x0Oh3q6urg4eGBkpKSNf1+rVYr9Ho99Ho9DAbDvKPRaASA8yb89iLAy8trWWHf0dGBqakpbNmyxaGuU61WixMnTuCqq65ymPRFRxPNbW1tsFqtKC4uZtuUi2JwcBB9fX3Lbi1Fr9G3dzItdEBZrVa4u7szApo+0s3Ly2tNxc3Y2Bja2tqQlpaG1NRUpxJSwLltpNra2uDu7o6CggKnjKAvBUVRkMvl6Orqgs1mQ0ZGBmJjY53uO16ISqXC4OAgJiYmEBkZidTU1CUj6hRF4ejRo3j66afR19eH73//+7jvvvuccgkYz+rCi2YeTlFdXY2f/OQnqK2txSOPPIKHH36Y6fisViuGh4cxODgILy8vpKWlcari43qhVqvR3d0NhUKB1NRUJCUlOb3DwB6dToe2tjao1WpkZWUhLi7Oqa4Bej9nhUKB0tLSJZ1HK4EgCOj1euh0Ouh0OuZcr9fDbDbD3d2dmagvnMDTazcvFYqi0NDQAIPBgE2bNjnUNVpTUwN/f39kZWWxbcqKcCTRbLFYcOjQIWzevNmh0kSlUilaWlqwcePGy/pNAue+r4WOKvrcYDBAIBDME9G+vr5M8/DwuOT+jiAIdHR0YGJiAsXFxZe8JIqrGAwGdHR0YGZmBpmZmUhMTHSqseFCzM3NoaurCxqNBmlpaUhISOB0ka+1wGAwYHBwEKOjowgJCUFaWhpCQkKYv588eRJPP/002tvb8cQTT+CBBx5wuGwoHvbgRTMPJzl16hSefvpptLa24qGHHsL1118PuVwOf39/pKWlITw83KUGw8WYnZ1FZ2cnjEYjMjIyEBcX53TrfZeCoihMTk6io6MDnp6eyM3NdaqUbYqiMDQ0hO7ubqSnpyMlJWXZ691isUCr1UKr1TICWavVwmAwQCQSMRNuegJOH9d6Sy+CIFBTUwOBQIANGzY4zPU5OzuLuro67Ny50yHEviOJ5r6+PszMzGDTpk1sm7Ji5HI56urqUFpauuZrYkmShMFgOM/JpdPpYDQa4ebmBl9fX/j5+TG/az8/P/j4+Cz7+9LpdGhoaIBAIEBpaalDpcVfCPsspJiYGGRlZTlUdsvlotVq0dPTg+npaSQnJyMlJYXz/cBaYzabMTg4iOHhYQQEBEClUuHVV19FY2MjHnvsMTz00EMOVbeChxvwopmH0xw/fhw/+tGP0NXVhf379+P73/++Q0Un1hq6ynRPTw9IkkRaWhokEonDiJPLxWazob+/H4ODg4iNjUVmZqZTTZbm5ubQ0NAAX19fFBUVQSgUQqPRMAKZPjebzfD09Jw3kaYn056enqw6mKxWK86ePcvs/eoIzi56O6TY2NiL2j6LLRxFNBMEgcOHDzvUtl50Rfj8/HxIJBJWbaH3El/oHNPpdADA/Ob9/Pzg7+/PiGmZTIbW1lbEx8cjKyvLacYHiqIwPT2N9vZ2uLu7Iy8v77KzABwJnU6H3t5eTExMIC4uDmlpafDy8mLbLE5x9OhRPPXUU2hvb8fDDz+MRx991Kkc7DzrCy+aeTgPRVE4fvw4fvrTn6KlpQUPPfQQHnroIZcaHC8ERVGQSqXo7e0FAKSlpSE2NtZpJkcXQq/Xo729HUqlEpmZmUhISHAIcbYUFEVBr9dDrVZDpVJBKpXCZDIBADw8PJgJsf3kmMtiid7TNjw8HLm5uQ7x3dBbnl155ZWcT3F0FNE8NDSEsbExbN261SGuAa1WizNnzlz03uPrDd1f2DvSaDFNT/FCQ0MRGRmJgIAA+Pv7c/o6WQk6nQ4dHR1QKpUut+eyXq9Hb28vZDIZJBIJ0tLSnCpz4HKhKArHjh3DT3/6U7S2tuLhhx/Gd7/7XX7OyHPZ8KKZx6E4efIknnvuOdTV1eGBBx7Ao48+uuJq264ASZKMeBYIBMjIyEBMTIzLTCampqbQ0dEBNzc35ObmzlvLxFVIkoRGo2EEslqthkajAUVRjCj29/eH0WjEyMgI0tLSkJaW5nDfqcFgwOnTp5GQkID09HS2zbkg9MQrJSUF8fHxbJuzLI4gmkmSxJEjR5CdnY2YmBi2zbkgRqMRp06dYooOOhoajQb19fUQCoWIj49ntoPTaDQwmUzw8vJCYGAgAgICmKMjFL6zWq3o7+/H0NAQJBIJMjMz13yZCVcwGAzo7e2FVCpFTEwM0tPT+eJVdlAUhUOHDuGnP/0puru78eijj+LBBx90qUJwPGsLL5p5HJKqqio899xzOH36NO677z489thjDpPutx6QJInx8XH09vZCJBIhIyMD0dHRDie0LgX79W0RERHIysrizMSCJElotVrMzc1BpVJBpVJBo9FAJBIxE1e6+fr6npcpoFarUV9fDy8vLxQXFzvEJNcejUaDM2fOMHtuc52xsTH09fVhx44dnP7tOIJopvujK664gvMZMCaTCWfOnEFYWBjy8vI4/d0vhKIojI2Nob29HUlJScjIyDjv8zabzdBoNIyTTq1WQ6fTwcPDA4GBgQgMDERQUBACAwM5s9yFfl/d3d3w8fFBbm6uyyzVMhqN6Ovrw9jYGKKjo5Genr7ibZVcAYqi8Nlnn+GnP/0phoaG8Nhjj+G+++7j1yzzrDq8aOZxaOrq6vDcc8/hyJEj2LNnDx599FGHiGKtFyRJYmxsDL29vXB3d0dGRobLVBw3mUzo6emBVCpFYmIi0tLS1lVQUBQFo9EIpVIJlUqFubk5qNVqCAQCZmJKN29v7xV/JzabDa2trZDL5SgoKEBUVNQav5PVZW5uDlVVVcjOzkZCQgLb5iwLHR3NyclBdHQ02+YsCddFM73EJikpifPfudlsxtmzZxEQEICioiKH6istFgtaW1uhUChQVFR0UdWxrVYrI6Tp/kqv18Pb25sR0bSQXu/lCnK5HJ2dnbDZbMjOznaZMcxoNKK/vx+jo6OIiopCeno6X+nZDrPZjD//+c/41a9+BblcjieeeAL33nsv71DgWTN40czjFLS3t+OVV17Bn//8Z1x99dV4/PHHsXnzZpcYWFcCQRAYHR1Ff38/3NzckJaWhpiYGM5HfFYDtVqNjo4OaDQapKenIyEhYU3eN0EQUKvVUCqVUCqVmJubg9lshr+/PzPhDAoKgq+v76pcl1KpFG1tbYiOjkZOTo5DVHmmUSgUqK6uRl5eHuLi4tg2Z1mGhoYwOjqKbdu2cbY/4bpodpT14XTROm9vb5SUlDhU/yiXy9Hc3IzAwEAUFBSsSoTYYrHME9Fzc3OwWCyMiA4ODkZwcPCaFZ/SarXo7OyEQqFAeno6EhMTOX39rBY6nQ4DAwMYHx9HZGQk0tPT+aipHXNzc3j77bfxm9/8Bv7+/njsscdw5513OlzmFY/jwYtmHqdiYmICr732Gt58802kp6fjsccew4033uhQgmItIQgCUqkU/f39oCgKKSkpiIuLc/qJCF1ltbOzEwCQnZ2NiIiIyxJBVqsVc3NzUCgUUCgUmJubg5ubG4KDg5kJZWBg4Jpee0ajEU1NTTAajSgqKnKoQiczMzOora1FYWEhp9e40hWf8/PzORvV57JopigKJ0+eRFxcHJKSktg2Z0msViuqq6shFotRVlbmMIKZIAh0d3djZGQEOTk5a1oQi6IoGAyGeY5BtVoNLy8vhISEMO1yHYNmsxm9vb0YHR1FfHw80tPTOZMmvpao1Wr09/djcnISMTExSE1N5SPLdoyMjODXv/41fvvb36K4uBiPP/44rr/+eof5rfI4Prxo5nFKtFotfv/73+PVV1+FQCDAI488grvvvptP2/kSkiQxMTGB/v5+mM1mJCcnIyEhgXMT7tWGJEmMjo6ip6cH/v7+yM7OXvG6OKvVCoVCgdnZWSgUikUniz4+PusejaQoCgMDA+jt7UVqaipSU1MdZhIxPT2N+vp6FBcXc1aQAsDg4CDGx8c5W/WZy6J5cnISra2tuOqqqzjrnLPZbKitrYVAIEB5eTln7VyIRqNBY2MjhEIhiouLWRnfFnMeuru7Izg4GKGhoQgNDYWfn9+KfjcEQWBoaAh9fX0IDQ1FVlaWS4hGpVLJ7F8eHx+PlJQUvhq2HQ0NDXj55Zfx97//HV/72tfw2GOPoaysjG2zeFwQXjTzODU2mw2ffPIJXnrpJQwODmL//v148MEHOT1BX0/oCGxfXx90Oh2SkpKQlJTk9NVIrVYr+vr6MDw8jMjISGRkZJw34SQIAkqlEjMzM5idnYVKpYK3tzczEQwJCeHUnphqtRoNDQ1wd3dHUVGRwziIJiYm0NTUhJKSEkRGRrJtzqLYbDYcOXIEBQUFnLSRq6KZjjJLJBLOFn6jBTNJkqioqHCIrCSKojA0NITu7m4kJycjPT2dM44ygiCgUqkYB6NSqYSbmxtCQ0MRFhaG0NDQ8woz2tfe8PDwQHZ2NsLCwlh6B+sDRVGYmZlBf38/VCoVEhISkJyczKcYfwlJkvjPf/6DX/3qV2hoaMC3v/1tPPTQQ0hMTGTbNB4XhhfNPC4BRVE4deoUXn75ZRw+fBi33347HnvsMWRnZ7NtGiegKAoKhQJ9fX1QKpXMAM4lUbgWGI1G9Pb2Ynx8HBKJBDExMVCr1ZDL5VAoFBCLxcxkLywsjPOfB0EQ6OzsxNjYGLKyspCYmMjJyOhCHEE4DwwMQCaTobKyknOfKVdF8/T0NJqbm3HllVdyUozSgpmiKJSXl3Pqs1sKvV6P5uZmZkkG17fVIwgCc3NzmJ2dxczMDObm5uDl5YXw8HCEhYXBZrOhr68PAJCZmen0uzxQFIXJyUn09/fDYDAgKSkJiYmJTu+oXikmkwnvv/8+fvWrX0Gj0eC73/0u7rnnHgQFBbFtGg8PL5p5XI/u7m68+uqreP/997F9+3Y89thjuOKKK5x6oL4Y5ubm0N/fj+npaURHRyMlJcVp9zm0Wq2YmZmBTCbD1NQUSJKEt7c34uPjERUVtWpFu9abmZkZtLS0wMvLC4WFhZzZcms5aOHM1VRtm82Gw4cPo7CwkHPCnouimY4yx8bGIiUlhW1zzsNeMG/YsIGTot4eiqIwPDyMrq4uxMXFISsri/M2L4bNZsPs7CxGR0chl8tBkiR8fHwgkUgQGRkJf39/h+xzL4TNZsP4+DgGBwdBEASz/7sjfodrwezsLN5880383//9H8LCwvD444/jm9/8pkusZedxHLiRz8PjUNx7770QCAT49a9/Pe/+hIQECASCee2Xv/zlvMe8++67iI+PR2FhIWpra9fR6v+SmZmJd955ByMjIygpKcEtt9yCoqIivP/++zCZTKzYxCWCgoJQVlaG7du3w83NDadPn8bZs2cxPT0NZ/Cx6fV6DA4O4uzZs/j888/R09MDLy8vlJeXY/PmzfDx8WGKsRAEwba5l0RYWBi2b98OPz8/HD9+HMPDw5z/7qKjo1FUVITGxkZMTk6ybc550FXne3p6OP9ZcoGpqSmYTCZObjHlaIJZr9ejqqoKg4ODKC8vR15eHudtXgqtVovBwUEoFApkZGTgiiuuQHJyMlQqFU6fPo3Dhw+jtbUV09PTDtv/2mMymdDd3Y1Dhw5hdHQU6enpuOqqq5CcnOyw3+Fq0tPTg/vvvx9xcXE4efIkDhw4gPb2dtx11128YObhHHykmeei+Pvf/45nn30WMzMzeOKJJ/Dwww8zf0tISMC3vvUt/M///A9zn5+fHxPlGhsbw44dO/Dee+9BJpPh6aefRldX13q/hfPQ6/U4cOAA/vd//xdKpRL79u3DPffcw9k1eOuNxWLB8PAwhoeH4e7ujuTkZEgkEocplkNRFFQqFSYmJjA1NQW9Xo/Q0FBERkYiIiJi0SjszMwMurq6YDQakZaWhvj4eId5vwuZmZlBc3MzfHx8UFhYyPkCM5OTk2hsbORkxJkgCBw5cgS5ubmc2reZa5Fmel/mhIQEzlXMttlsqKmpYYp+cVm4UBSFkZERdHZ2QiKRICsrixPf76WgVqvR29sLuVyOpKQkpKamnvdeCIKAQqHA1NQUpqamYLFYEBYWhqioKERGRjpUCrNGo2GWdISFhSE5ORmhoaFOGUW/WCwWC/7xj3/gzTffRHV1Nb7xjW/gscceQ2FhIdum8fAsC3dHCx7OIZPJ8OCDD+KLL77A9ddfv+hj/Pz8lkxd1Gg0CAwMRF5eHiIjI2E0GtfS3BXj4+OD++67D/v378fx48fx5ptvIisrC9u3b8f+/ftx/fXXc3pitdaIxWKkp6cjJSUFMpkMAwMD6O7uRmJiIhITEznpDabXaE9OTmJiYgI2mw2RkZHIzMxEWFjYBSeeYWFhqKysxOTkJHp6etDf34/U1FSHFM901LmzsxPHjx9HVlYWkxXCRaKiolBcXIzGxkbObUclEomYaHNUVBRnP0O2oX9z8fHxbJsyD6vVipqaGgiFQs4LZr1ej5aWFuj1epSVlSE8PJxtky4JjUaD3t5eTE1NIT4+HldeeeWSxa5EIhHCw8MRHh6O3NxcaDQaTE1NYWhoCC0tLQgNDUV0dDQiIyM5WTCLLu41MDAAhUIBiUSCbdu2uUQF8JUwNjaGd955B7/97W/h7e2Ne++9F3/5y1+cvugbj/PAR5p5VgRJkrjyyivx1a9+FQ899BASEhLw8MMPnxdpNplMsFqtiIuLw+23345HHnlk3sTkW9/6Fg4cOACxWIx3330Xu3fvZuHdXJjJyUn89re/xTvvvAMA+M53voNvf/vbnIt8scFiE4OkpCT4+/uzbpdSqYRUKsXk5CQoikJkZCSio6MRFhZ2ydVl6cItPT09sFqtSEtLc9i9reVyOVpaWuDt7Y38/HxOT+bo7ahyc3M5Jb5IksSRI0eQlZWF2NhYts0BwK1IM0mSOH78OLONHVcwm82orq6Gp6cnSktLOfv7JUkSQ0ND6OnpQWxsLLKzs1n/Ti+FhWI5NTX1sgop6vV6TE5OYnJyEnNzcwgODkZMTAyio6NZd9wSBAGpVIrBwUGYzWZOO5TXG5Ik8cUXX+DNN9/EwYMHce2112L//v3YuXMnZyq+8/CsFF4086yI559/HsePH8cXX3wBgUCwqGh+5ZVXUFRUhODgYFRVVeHJJ5/Evn378Morr8x7LoVCAW9vb85XIgbOpfL95z//wVtvvYVjx45h165d2L9/P7Zv385HmXBuYjQ4OAipVIrg4GAkJiYiMjJy3QZDiqKg0WgglUohk8lgs9kQHR2NmJgYhISErKodFEVhYmICvb29Di2ebTYbenp6MDIywvl9nWdnZ1FbW4uMjAxOLZcYHR1FX18fduzYwYnPjkuieXR0FP39/bjiiis48dkA56rkV1dXw8/PD8XFxZyxayEqlQotLS0gCAL5+fkIDQ1l26SLxl4sx8XFIS0tbdXHeqPRiMnJSUilUqhUKoSHhyMmJgaRkZHrev0bDAYMDw9jdHQUnp6eSE5ORmxsrMONCWvBzMwMfv/73+Ptt9+G0WjEt7/9bfzP//wP4uLi2DaNh+eS4UUzz3l88MEHuOeee5jb//nPf3DrrbeiqamJWce3mGheyO9//3vcc8890Ol0TuFxHRoawttvv43f//73CA4Oxr333ou77rqL3woB56I4Y2NjTMGphIQExMfHr1kKnclkwvj4OMbHx2EwGBAVFYWYmBiEh4ev+YTYXjzbbDYmbZurE/GlmJubQ0tLCyiKQkFBAYKDg9k2aVHm5uZQXV2N5ORkpKWlccJZRZIkTpw4wZk1u1wRzfSa75ycHM6k1RsMBpw9exahoaEoKCjgxPWzEJvNht7eXgwPDzPXuaMJL41Gg76+PkxOTq6ZWF4MvV4PmUwGmUwGvV6PqKgoSCQShIWFrcl3TWdaDQ8PQy6XIyIiAklJSQgJCeHktbWeUBSFM2fO4M0338Tf/vY3bNq0Cfv378dXv/pV1p15PDyrAS+aec5Dq9Vienqauf3xxx/jRz/60TxRQBAEhEIhJBIJRkZGFn2ezs5O5OTkoKenB+np6Wtt9rphNpvx17/+FW+++Saamppw6623Yv/+/SgtLeUHTYrC9PQ0hoaGoFAoEB0djcTERAQFBV32Z0OSJKampjA2Nga5XI6QkBDExcUhKiqKlbWJtHju6elhthCJi4vj9DrJhZAkicHBQfT29nK60JBGo0FVVRVjIxd+Z5OTk2htbcWOHTtY/8y4Ipr7+/shk8mwdetWTnxHWq0WVVVViI6ORk5ODidsWohcLkdrays8PDxQUFDA+jKXi0WlUqG/v5+JLKemprJWbFCj0WBsbAxSqZSZn8TFxa3KlntWq5VxDNPr9RMSEhwiY26t0Wg0eP/99/HWW29BKpXirrvuwr333utU8z4eHoAXzTwrgC6oZM/VV1+NO++8E/v27VuyY/zggw+wZ88ezM7OOm00tr29HW+99Rbef/99pKamYv/+/bjtttvg6+vLtmmso9PpMDw8jLGxMfj4+CAxMfGSUtd0Oh1GR0cxNjYGNzc3xMXFQSKRcKYKNC2e+/v7YTQakZycjMTERNaF1MWg0+nQ2toKnU6H3NxcTha50ul0qKqqQnh4OPLy8liP7NNRlbCwMGRkZLBqCxdEs8ViwZEjR1BaWsqJwj5zc3OoqalBQkICMjIyOHc9m81mdHZ2YnJyEpmZmUhMTOScjUtBF1rs7++HQqFAXFwcUlJSONMnkyQJuVyOsbExTE1NISQkBAkJCYiKirrofkOj0WB4eBjj4+MICAhAYmIioqKiHC4TYC1oaWnBm2++iQ8++ADZ2dm49957ceutt3LmOuDhWW140cxzSSxMz66urkZtbS2zN2x1dTUeeeQRXHvttThw4AC7xq4DWq0WH374Id58800MDAzgG9/4Bvbu3YutW7eyPrlnG5vNBqlUiuHhYRiNRsTFxSE+Pn7ZIlR0VHlkZAQKhQKRkZFISEjg9JYdFEVBLpejv78farUaiYmJSEpK4mSV18WgKArj4+Po7OxEYGAgcnNzOef8odem+vj4oKSkhPWJq0KhQHV1Na666ipWl6BwQTR3dnZCrVZj48aNrLy+PXK5HPX19ZxbCw+c+50NDw+ju7sbYWFhyM3NdZhoJZ1J1N/fD41Gg6SkJCQlJXF6+RW9dGhkZAQEQSAuLg4JCQnLCjuCIDA5OYmRkRGoVCrExMQgMTERgYGB62c4R5mensaHH36IAwcOoL+/H7fffjvuvfdeFBcXs20aD8+aw4tmnktioWhuamrCfffdh56eHqZ65J133olHH32U0wPqakNRFFpaWnDgwAF8+OGH8PLywp133ok9e/YgLS2NbfNYhY5OjIyMYHJyEkFBQYiLi0N0dDST0myxWDAyMoLh4WEIBALEx8ev6drotYKOwszOzkIikbCasnixWCwWdHd3Y3x8nJPrKy0WC2prawEA5eXlrO/dWltbC09PT+Tn57NmA9ui2WAw4NixY9i8eTPrwmJ8fBytra0oKCjgTHVzGqVSiba2NthsNuTm5iIiIoJtk1YESZJMNo3ZbGYqoztSNo39WuTp6WlEREQgOTl53lpkjUaD0dFRjI+Pw8PDA/Hx8YiLi2O9j2Ebk8mETz/9FAcOHMChQ4ewadMm7N27FzfddBOnd2Dg4VlteNHMw7NGWK1WHDx4EAcOHMCnn36K4uJi7NmzB7feeqvTpquvFIvFgvHxcYyOjsJoNCIyMhIkSWJ6ehqBgYFITk5GZGQkZ6PKK0WtVqO/vx+Tk5OIjo5Gamqqw6xZnJubQ1tbGywWC3Jzc5fcf50NCIJAQ0MD9Ho9KioqWI3UabVanDhxAlu3bmXtu2VbNDc0NEAoFKKoqGjdX9uegYEB9Pb2orS0lFP7GlssFnR1dUEqlSI1NRUpKSmcckQtBUEQGB8fx8DAACiKYuo2OILty2E0GjE8PIyRkRF4eXkhMDAQGo0GGo0G0dHRSEhIQHBwsMOPP5cDRVGora3FgQMH8NFHHyE0NBR79+7FnXfeyaktAHl41hNeNPPwrANKpRIfffQR3nvvPbS0tGDXrl3Yu3cvrr76aocqHLXaKJVKdHV1QaFQQCAQwMfHB8nJyYiJiXGoKMaF0Ov1GBgYwNjYGEJDQ5GcnLxm1V1XE4qiMDo6iq6uLoSEhCAnJ2dViuqsBiRJoq2tDXK5HBs2bGDVGdHW1gaDwYANGzaw8vpsimalUomqqirs2LGDNecFRVHo6urC2NgYKioqWI9209j/foKDg5Gbm8uZ389ymM1mJuNHLBYjNTUVMTExTrPUiKIoqFQqDA8PQyaTAQCEQiFSUlKQnJzs0mPy2NgY3n//fbz33nuYnp7Grbfeir1796KiooLz4xUPz1rDi2YennWmp6cH7733Ht5//31YLBbs3r0be/fuZTW9c71RKBTo6+uDQqFAQkICM1GRSqUYHR2FTqdDTEwM4uPjV6XyNlcwmUzMZJTe1zMmJobzkRuz2Yzu7m5IpVKkpKQgJSWFExNLiqLQ09OD4eFhlJWVsbavLV0Eq6SkhJUIJ1uimaIonD59GuHh4awVQyMIAs3NzZibm0NFRQVn1uErlUq0t7dzMlNjKbRaLQYHByGVShEcHIyUlBSHcO6tFKvVivHxcYyNjUGn0yE2Nhbx8fEICAjA9PQ0+vr6YDAYHLKY4+Wg0+nwySef4MCBAzhz5gyuuuoq7N27F7t27XK4pVE8PGsJL5p5eFiCIAicOHECBw4cwCeffILU1FTs2bMHu3fvdpi1bhfL7Owsenp6oFarly0io1KpMDo6CqlUCk9PT8TFxSE2NtZhCuZcCDrtcXBwEFarFYmJiUhMTOT82jmlUomOjg6YTCZkZWUhJiaGExPq0dFRtLe3Iz8/HxKJhBUb6EyC7du3r/tnwpZolslkaG9vx5VXXsmKE8VsNqOurg4URaG8vJwT9TOMRiO6urowOTnJKQfTUlAUhdnZWQwODmJmZgaxsbFITk52mGUkF4IkSczMzGB8fByTk5MICAhAfHw8YmJizvte6HXPvb290Gq1SEpKQnJyslOKZ5Ikcfz4cbz33nv45JNPkJycjL179+L22293CAcPDw8b8KKZh4cDaLVa/O1vf2M8vTt37sSdd96Jr3zlKw6Rznch1Go1urq6oFQqkZKSgqSkpBVNRGw2G6ampjA+Po6ZmRmEhoZCIpGwtjfzakNX3B4YGMDc3BwkEgmSk5M5Ey1bDIqiIJVK0dXVBS8vL+Tm5nJijT5dMTk5ORnp6enrLlwJgsDx48eZKNV6woZoJggCR48eRXp6OitrHHU6HWpqauDv74/i4mLWszVsNhsGBwfR39+PqKgoZGVlcdrJR5IkZDIZBgcHYTQakZCQgMTERKeJLNrv2SwQCBAbGwuJRLJiZwDt4NVqtUhNTUViYiLr19hq0NnZiQ8//BDvv/8+zGYzdu/ejT179qCgoIBt03h4OA8vmnl4OMbo6Cj+9Kc/4U9/+hPGxsZw/fXX4+abb8Z1113ncAJar9eju7sbU1NTSExMRGpq6iVHU41GI6RSKcbHx2EwGBAdHQ2JRMLpbaguBrVajcHBQchkMoSHhyMxMZHTqZE2mw39/f0YHBxEdHQ0MjMzWRcJGo0GNTU1CA0NRUFBwbqvwZyamkJzczN27NixrlkDbIjmnp4eTE9Po7Kyct2vUYVCgdraWsTHxyMrK4vV3whFUZDJZOjq6oKHhwdyc3MRHBzMmj0Xwn69spubG5KTkyGRSJzCCWkymSCTyTA+Pg6dToeoqChIJJJL7kdpp2Z3dzfMZjMyMjIQFxfH2T55KTo7O/Hxxx/j448/xvDwML7yla9gz549uPrqq50yis7Ds1bwopmHh6NQFIXOzk785S9/wccff+xQAtpqtaKvrw/Dw8OIiYlBRkbGqgkqiqKgVqsxPj4OqVQKkUgEiUSC2NhYp9j+gq7sOjo6CrFYjMTEREgkEs5ObgwGA7q6ujA1NcWJysAmkwk1NTVwd3dHaWnpuopXiqJQU1MDHx8f5OXlrdvrrrdopreY2rhx47oLRKlUipaWFuTk5CAhIWFdX3shc3Nz6OjogF6vR1ZWFiQSCScFFUVRmJubw/DwMCYmJhASEoKkpCRERERw0t6LgSAIJhtJLpcjJCQEsbGxiI6OXrXfAkVRmJiYQHd3N9zc3JCbm4uQkJBVee61YqFQvu6663DzzTfj+uuv53QmEw8Pl+FFMw+PA+AoApqiKIyPj6Orqwu+vr7Izc1FQEDAmr0eSZKQy+UYHx/H1NQUAgICmAmTo6cZEgSBiYkJDA8PQ6PRQCKRIDExkbNrDRUKBVP4KCMjg1UBYbPZ0NjYCK1Wi/Ly8nV1puh0Ohw/fhyVlZVreu3bs96iua6uDm5ubuu6xRRd9G1oaAglJSWs1n3Q6/Xo6enB5OQkkpOTkZqayslILUEQkMlkGBoagl6vR1xcHBISEhzeuUhRFBQKBaRSKWQyGTw8PCCRSCCRSODt7b1mr0sQBAYHB9HX14fIyEhkZ2eznl1jDy+UeXjWFl408/A4GBRFoaOjgxkcuSKgtVotWlpaYDAYkJOTg+jo6HUVTRaLBTKZDDKZDEqlkok4REVFcb7A1oWgo0QymQzBwcFITExEZGQk57aAodc7d3d3w93dHVlZWQgPD2dFPFMUhe7ubgwPD6+7yOrs7MTc3Bw2btwIm80Gq9UKq9UKm802rxEEwRwJggBJkucdSZIERVHMOf3e6Eaj0+ng6+vLfNYCgYBpQqHw/7d3rzFtnYcbwB9DsLkZg218BWwSAgQSkpTcmqZNIlJVWpJ206atlbYPXTRVkzapXadO3YdplSKtW9Xuw7RqUrWu2l/rqk3TtKVpOiVZL7k1IQlpKAFDwsWA8d3G9/v5f6h8hgO0SRNsA89PemUbcznGPue8z3s7KCkpyblfWloq3t5e1qxZM6+UlZVBKpXC4/Ggt7cXPT09eWuYSqVSuHr1KmZnZ7Fz586CNRwlEol5I2iWMqR9VZFIBGNjY7BarZDJZGhubkZDQ0PRjla5E9ne8uwxXhAEcYpOvq+wMHext/b2dqxdu7Zgx+K5QXl0dFSsCzAoE91/DM1Ey9jcAP23v/0Nk5OTeQ/QmUwGIyMjGBkZgdlsRnt7e8F7XaLRKGw2G6ampjA7OwuNRgOj0QidTresK47xeBxWqxVjY2MQBAFmsxkmk6noetXT6TTGxsYwPDwMhUKBzs7Ogl07d3JyEp9++ina29uxbt26e6pcp1IpxGIxxONxsSQSCfE2W+LxOGKxmPhzEolEDJ5zg+jckJq9PzfQ3h525wbhueE4lUqht7cX27dvR2lpqRims8F6ofA9N5zfXrKhfm7QT6fT4uspLS1FRUUFpFJpTpHJZGKRSqUoLy+HTCa7p/95JBLBxYsXIZVK8z7cPiudTmN0dBTDw8NQKpXo6OjI2yiCO5Vd+XlsbAxOpxNarRbNzc3Les0HQRAQCAQwNTUFm82GZDIJvV4Po9EItVpd8EZDj8eDvr4+lJWVYcuWLXn7TDAoExUGQzPRClGIAO33+9HX1wcA2LJlS1Gsony7cDgs9k6EQiFotVo0NDRAq9Uu29VQBUGA3W7H2NgYPB4PtFotzGZz0S0cNnduu16vR3t7e0FGQvh8Ply8eBEajQabN29e8H1PJpOIRCKIRqM5JRaLibfpdBoSiUQMg9lwmL3NlrKyMqTTacjlckilUpSWli7p+yIIAlKpFNasWbNkfyeTySCZTCIQCKC0tBSpVGpeQ0H2NluSySQkEokYoCsqKlBRUZFzv7KyEuXl5QsGII/Hg0uXLsFgMGDTpk15D0nZ6SaDg4OQyWTo7OxEfX19Xrfhy8RiMVitVkxMTCCVSsFkMqG5ubmohg3frWAwKB6zo9GoGJTr6+uL7pidTqdhsVgwOjoqrty/FJ/TGzduiNOzRkdH8bWvfQ3f/va3GZSJ8oihmWgFuj1AW61W9PT04PDhwzh48CCMRuM9//6bN2/CYrGgpaUFra2tBW/1vxOBQEDsgY7FYkVdGbtT4XAYExMTsFqtKC0tRVNTE5qamoqq0hyNRjE4OIjp6WmYzWa0trbm/Zq62R7LTCaDpqYmJBIJhMNhRCIRhMNhMXRWVlbOC3fl5eViKSsrK6qGiWKWTqfFXve5jQ+3N0xkGyKqqqpQVVWFyspKRCIRTE5OYsOGDWhpacnrdguCAIfDgcHBQaRSKWzYsKForkkO/G9V5/HxcTgcDqjVapjN5qKcsnGnQqEQbDZbTuOm0WiEVqst+MilOzE7O4urV6+ipKQE3d3d9xxkk8kkzp07h2PHjuHYsWOYnJxkUCYqMIZmohUuO7fz3XffxbFjx3DhwgVs3rwZhw4dwuHDh/HAAw/cVUUrFovh6tWrCIfD6O7uLurLqywmuwL39PQ0bDYb4vE4dDod9Ho9NBrNshzCnclkYLfbMTExAZfLBa1WC5PJVFQr5M7OzmJwcBButxvr1q3DunXr7vtw23Q6jVAohGAwiGAwiFAoJJZMJoPS0lJUVlZCpVKJAS17uxzf9+Uuk8kgFouJjReRSAShUAgOhwPA5+9neXk5qqurUV1dDblcLpZ7Hfq9EJfLhcHBQYTDYbS2tsJsNhdNg1o0GhV7lTOZDEwmE5qamopmIci7IQgCgsEgbDYbZmZmEAqFUF9fv6yn0aTTady4cQNWqxWbNm2668UQvV4vTpw4gXfffRfvv/8+ZDIZDh06hEOHDuHAgQMMykQFxtBMtMp4PJ6cE3NlZSUOHjwonpi/qALmdrvR29sLjUaDrq6uZVmxuV123ly28hYOh6HRaKDX66HT6ZblImKRSETsfZZIJGLvc7EsWuT1ejE4OAi/34+WlhasXbv2rj9LgiAgFothdnYWs7OzCAQCCAQCCIfDKC0thVwuF0NWNnBVVVXlzAem4pWtmiQSiZyGj2xjSCQSQVlZGWpqasSiUChQU1PzlUKux+PB0NAQZmdnxc9kMfRwZjIZOBwOTExMwOl0QqPRiI1hy61XObuY18zMDGZmZhCLxaDVaqHX66HValfE+QQAHA4Hrl69Cq1Wu+h0EOB/K8JnG7TPnz+Prq4usUG7u7t72b3HRCsZQzPRKpZMJnHmzBnxpD05OYmenh6xdbuxsRHA5yf3sbEx3Lhxoyiuj7qUgsEgZmZmYLPZEAgEoFarodfrodfri27BrS+TvSTXxMSEOIyzqakJer2+KHrPXC4XhoaGEAqFsH79epjN5kWDSjQahd/vzymJRALV1dViWMqWiooKBuMVLpVKIRgMio0lgUAAs7OzSKVSkMvlUCgUqKurQ21tLRQKxaLhw+/3Y3BwEB6PB+vWrUNLS0tRhLdAIIDJyUlMTk6ipKQETU1NMJlMRTXt4k5kMhl4vV6xUTKVSkGn08FgMKC+vr4oGiaWQiwWw6VLlyAIAnbs2CG+b4lEIuecOz09nXPObWhoKPCWE9FiGJqJCMDnwdhisYgn83PnzmHjxo04ePAg2traoFarsXPnTqhUqkJvat5EIhGxV8Tr9aKurk7sgV5uQ+Wi0SimpqZgtVoRi8VgNBrR2NgIpVJZ0ICZnZ85ODiIWCyG1tZWNDY2IhgMwufzwev1wuv1IhaLQS6Xo7a2Viw1NTUrttJNd08QhAUbV9LpNBQKBZRKpVgSiQSGhobgdDrR3NyMlpaWvM+zv10ikcDU1BQmJycRCASg1+vR1NRUdAv8fZl0Og2Xy4WZmRnY7XZIJBIxKBfDqtf5kk6ncf36dQwPD8Pr9eKDDz7Af/7zH1RVVYkhuaenZ1kOrydajRiaiWhBXq8X77//Po4dO4YTJ05AJpPh4MGDOHz4MHp6egp2rdRCicVisNvtsNls8Hg8qKyshE6ng1arhVKpXDYVQUEQ4Pf7YbVaMT09DalUisbGRjQ2NhZs+HY6nYbP58P4+DjsdjvS6TTKysrEgJPtMSyGHkBaXgRBQDgchtfrFRthAoEAAKC6uhpmsxkGg6FgPbjZ0SBWqxUOhwO1tbVobGyE0WhcVp/3aDQKh8MBh8MBl8sFqVQKvV4Pg8FQ8Ia5fBMEAQMDAzh+/DiOHTuGTz75BF1dXTh8+DAef/xxbN26ddmcL4jofxiaiehLJZNJnD9/XlzJc3R0FDt37sSBAwdw4MAB7Ny5c1lV8O5VMpmEy+WC3W6Hw+GAIAjQaDTQ6XTQaDTLZh50Op2Gw+GA1WqF0+mESqVCY2MjDAbDkvbgZhcBcjqdcLlc8Hg8WLNmDdRqNZRKJSoqKqDT6VZVRZvyJxwOw+12IxQKwePxwO/3o7KyEvX19WJZ6uPZ7OwsJicnMTU1hZKSErHharmMYMkupmi322G32xEIBFBXVwetVgudTge5XL6q9t/p6WmcPn0ap06dwqlTp+D3+3HgwAEcOnTovlyxgogKj6GZiO7axMQETp8+jZMnT+L06dOIRqPYt28fDhw4gEcffRQbNmxYNRWm7OI2DocDdrsdwWAQSqVS7IWurq5eFv+LWCwmDg0Nh8PQ6/VoaGhAfX39fekVSaVScDqdcDgccDqdSCaTUKvVYkhZbZVsKh7JZBJutxtutxtOpxPhcBhKpVIMgPdrH45EIpiensbU1BTC4TAMBgMaGxuhVquXxWc/lUrB5XKJx7p0Og2NRgOtVgutVlvw4e35FAwG8eGHH4oheWhoCNu2bcOjjz6KAwcO4MEHH1xV/w+i1YChmYjuSSaTQX9/P06ePIlTp07h448/Rm1trRige3p6YDAYCr2ZeROJRMRKpdvtRkVFhVipVKlURbEA1xeZezmuqakpZDIZGAwGNDQ03PUwy0QiIc4Jd7lcYg+yRqNZFv8LWp3C4bDYwDP3c2swGFBXV3dX+0A8HhevDe/z+cTLKun1+mUxOmeh/0W2MUGlUq2aYcbJZBKXLl0Sz3MXL15Ec3OzeJ7bt28f6urqCr2ZRLSEGJqJ6L6Kx+M4f/682AJ/+fJltLe3iy3we/fuhVwuL/Rm5sXcnhmn04l4PA6VSgWNRgONRlP0vauCIMDj8YjXsy4tLYXRaITRaIRCoVhw25PJJGw2G6anp+F2u6FQKGAwGMQhm0TLSXYfzi5qVVpaKjYi1dbWLroPzMzMYHp6Gi6XC3V1dWhoaIDBYCj63sdsr3t26kQkEhGPWfez173YCYKAwcFBnDp1CidPnsSHH36I8vJycUrSgQMHYDKZCr2ZRJRHDM1EtKR8Ph8++OADsfIxPj6OXbt2iRWPHTt2LIsel3slCAJCoRCcTiecTic8Hg/KysrEAF1fX1/Uc6EzmQxcLhempqYwMzODiooKNDQ0wGg0orKyEg6HA5OTk3A4HKipqYHRaITBYCiaa0MT3avsPmCz2WCz2SCTycS5yDKZDA6HA9PT07Db7aiurs7ZP4pVdmRJ9rjk9XpRWVkpHpfUavWqWaHeZrPlzEv2+Xx45JFHxHNVV1fXqulZJ6L5GJqJKK/Gx8fFSsnp06cRj8exZ88e7NmzBw8//DC2b9++7K6H/FWk02l4PB64XC44nU5xIZ36+npoNBrU1dUVbQUtlUrB4XBgfHwcbrcbEokE5eXlaGxsRENDA3uUacVLp9Ow2+2YmpqCw+EAAEilUjQ1NaGxsbGo94FYLCb2JDudTmQyGajVajEor4ZLIAmCAKvVijNnzuDs2bM4c+YMhoaG0N3dnTMveTWci4jozjA0E1HBZDIZfPrpp/j444/FiovP58P27dvFEL179+5VMVcsGo2KlViXyyVWZLOLZRXLUO7sdZVHR0fhdruh0WigVCrR0tJSFNtHlG9+vx8TExNwOp1Ip9Mwm80wm81FE7iSyaTYQOd2uxEIBFBbWyuG5GJuoLtfMpkMBgYGckKy3W7HAw88IDba7t27F0qlstCbSkRFiqGZiIqGIAi4deuWWLE5e/YsRkZGsHHjRjFE79mzB42NjYXe1CWVvZZydkVfj8eD0tLSnBBdVVWV15CaTqcxNTWFmzdvIplMFl0wICq0bIPS2NgYXC4XGhoasG7durxf0z6VSsHr9Yoh2e/3o6qqCvX19eIxpNjnVt+reDyOy5cvi+eSc+fOIZlM4sEHHxTPJTt37lwVvepEdH8wNBNRUXM4HDh37pxY+enr60NDQ0NOiN6wYcOK7inJZDLw+XxiiPZ6vZBKpTkheqnmTabTaYyPj2NkZARSqRQtLS1oaGhY0f9vonsVDAZx69YtTE5OQqPRoL29HQqFYkn+VjqdhtfrFY8PPp8PFRUV4vFBrVajoqJiSf52sfD7/bhw4YJ4nrh06RIUCkXOeWLLli2rZn42Ed1/DM1EtKwEg0FcvHhRrBx98sknKC8vx0MPPSRWjrq7u4t6Ua179WWVZJVKhYqKinvqic5kMpiYmIDFYoFMJkNbWxv0ej2HYBPdhWg0ips3b2J8fBxarRYdHR2orq6+p9+ZTqfh8/ng8XgWbERTq9Urvgd1enpaHGZ99uxZ9Pf3Y+3ateI5YM+ePVi/fj2PV0R03zA0E9Gylkwm0dfXl1OBCgaD2LJlC7Zv3y6Wtra2Fds7evtwzNnZWchkMqhUKiiVSqhUKtTU1NxxBdJut2NgYACCIKCjo4NhmegeRaNRDA8Pw2q1wmQyob29/Y4b9hKJBLxeLzweD7xeL/x+P8rKyqBSqQo2XSOf/H4/rly5gt7eXrFMT09jy5YtOSFZp9MVelOJaAVjaCaiFUUQBIyMjORUsPr6+rBmzRp0d3fnBGmTybQiK5qpVAo+ny+noi2RSMQArVQqUVdXh9LS0pyfi0Qi6O/vh8fjQXt7O8xm84ptaCAqhGAwiIGBAfh8PnR0dKCpqSnnGCQIAqLRqLjfejweBINBVFdX5zSCVVZWrshjVyQSQV9fH3p7e3H58mX09vZiZGQEJpMp59jd3d1d1CuUE9HKw9BMRCteKpXCwMBATpDu7+9HXV0dtm3bllMZ02q1hd7c+y6TySAQCORUxJPJJGpra6FUKqFUKhEKhWCxWGA0GtHR0bHiFwoiKhRBEGC329Hf34+qqiqsW7cOkUhE3Dfj8TgUCgVUKpUYlFfi/phMJvHZZ5+Jx+RLly5hYGAAarU655i8bds21NfXF3pziWiVY2gmolUpGo3i008/zQnSFosFDQ0N8ypsS7WAT6EIgoBIJAKPxwOn0wm73Q5BELBr1y5WTonyJJlM4sqVK3A4HJDL5TAajeIokJW2YFUmk4HFYhF7j3t7e3Ht2jXIZLJ5DZcNDQ0rshediJY3hmaiZWJwcBA/+9nP8NFHHyGVSqGjowP/+Mc/0NTUBACIxWJ4/vnn8c477yAej+Oxxx7D66+/ntNz+u9//xs//elPIZFI8Oqrr+LQoUOFejlFKRAIzJs7NzExgdbWVmzbtg2bN29GV1cXurq6VsQ8X7fbjcuXL0OtVmPjxo28fBRRnmUyGTgcDly7dg06nQ5dXV3zpk0sN7FYDIODg7h+/TquX7+Ovr4+XLlyBYlEAlu3bs0JyOvXr+cUECJaFhiaiZaBW7duYceOHThy5Aieeuop1NTUYGBgALt27YJGowEA/PCHP8Tx48fx1ltvQaFQ4Ec/+hFKSkpw7tw5AJ9ft7KlpQV/+tOfIAgCvv/97+PWrVsrepXp+8HpdOLy5cu4cuWKWAkcGRlBXV2dGKCzpbOzc8ku/XS/TUxMoL+/H52dnTCbzcu+AYBoOYtGo+jt7QUA7Ny5c1kMxxYEAZOTk+JxMVuGh4chl8uxadMmdHV1YfPmzdi+fTs6OztRVlZW6M0mIvpKGJqJloEnn3wSZWVl+L//+78Fn5+dnUV9fT3efvttfOtb3wIADA0NYcOGDbhw4QJ27dqFQCCArq4uXL58GQCwfft2XL9+nYupfAWRSAQ3btyYV1n0er1Yv349urq6xApjV1dX0S2oZbFYxIYYtVpd6M0hInx+Kam+vj74fD489NBDRdUAFwwG8dlnn+Uc7/r7+xEKhdDW1javAZFDrIlopWFoJipymUwGCoUCL7zwAs6ePYu+vj40NzfjxRdfxNe//nUAwH//+1/09PTA5/OhtrZW/FmTyYRnn30Wzz33HADgpZdewtGjRyGRSHD06FG88MILBXhFK5MgCJiZmUF/f39OxXJwcBAymSwnRG/atAmbNm3Kea/yZWhoCOPj49i9ezdqamry/veJaHGCIOD69etwOBzYs2dP3oNzOp3GrVu3coLx9evXMTo6Cq1WOy8ct7e3c1oHEa0KDM1ERc5ut0Ov16OyshJHjx7F/v378f777+PnP/85PvjgA+zduxdvv/02nn76acTj8Zyf3bFjB/bv349f//rX4tdmZ2dRUlLCHuY8SSQSsFgsORXQ69evY3p6Gk1NTWhvb0dbW1tOWapemtHRUVgsFuzZs4fvP1GRygZnt9uNhx9+eEmm0ITDYYyMjMBiseSUGzduIJPJoLOzMyccb9q0SZwKRES0Gq2s5RmJVoC//OUveOaZZ8THx48fBwA88cQTYo/xli1bcP78efzhD3/A3r177+r3r7SVoIudVCoVe5bn8ng86O/vx9DQECwWC9577z389re/xdjYGMrLy9Ha2jovTLe2tn7lsOtyuXDjxg3s3r2bgZmoiEkkEnR1dYmLEe7evfsrNaJlMhlYrdZ5wdhisWBqagoKhUI8tnR0dOAb3/gGOjs7sX79+hW3ejcR0b3iUZGoyDz++OPYuXOn+Li+vh5r1qxBR0dHzvdt2LABZ8+eBQDodDokEgn4/f6cIb8OhwM6nS4v2013R6VSYd++fdi3b1/O1+PxOG7dupVTwT158iQsFgu8Xi8MBsOCgdpsNi+66m48HseVK1ewceNGKJXKPLw6IroXEokEW7duxUcffYTh4WG0tbUt+r1+vx/Dw8PzgvHIyAhSqRTWrl0rHieefPJJsQFOo9Fw3jER0R1iaCYqMnK5fF5P4Pbt22GxWHK+Njw8DJPJBADo7u5GWVkZTp8+jW9+85sAPl/syWq14sEHH8zPhtN9IZPJ0NHRMa+RBPj8ElFzK8cff/wx3njjDdy8eRMSiQQtLS1ihbi5uRlmsxnNzc3weDyoq6sTPy9EVPzKysqwbds2nDlzBlKpFE6nE+Pj4xgfH8fNmzfF44DT6YRGoxEb03bs2IHvfe97aGtrw9q1a7liNRHRfcA5zUTLwD//+U985zvfwe9//3txTvOzzz6LDz/8EHv27AHw+SWn3nvvPbz11luoqanBj3/8YwDA+fPnC7nplAepVArj4+M5PUzZyvX4+DhisRj0ej3MZrNYsqHabDajqalpWVzihmglEgQBbrdb3F/HxsZy9t+xsTHEYjHodDpxn53be9za2oq6urpCvwwiohWNoZlomXjzzTfxq1/9ClNTU2hra8NLL72EJ554Qnw+Fovh+eefx1//+lfE43E89thjeP311zk8e5XLZDI5PVS3V8onJiYQj8dhMBjmhem5oZrX8yb6agRBgMfjWXQfHB8fRyQSgUajWXQfNJlMqKioKPRLISJatRiaiYhWsUwmA4fDsWBFPhuqk8kkjEYjTCYTDAYDDAYD9Hr9vPsKhYJzJGlVSSaTsNvtsNlsmJmZybm12WyYmprC+Pg4QqEQ6uvr54XhbEA2mUxFdV1mIiLKxdBMRESLymQymJmZEQN0NgzMDQjT09OIRCIoLy9fNFDPva2trWW4pqI2NwwvFIizty6XCxKJBFqtdsHPvdFoFINxVVVVoV8WERF9RQzNRER0TwRBQDAYzOlhW6jXzWazLRiu9Xo96uvroVaroVKp5hUOS6V7lclk4Pf74Xa74fF45pWZmZmcz+xCYXihUGwwGKDRaHiJJiKiFY6hmYiI8iIbrhfrsZsbYtxuN2ZnZwEAFRUVUKlUi4bqhZ7jUPGVKx6PLxh8s5+bhb7u8/mQyWQglUoX/Pzo9fp5oVir1TIMExERAIZmIiIqUqlUCl6v964DUiqVQmlpKZRKJVQqFeRyOaqrq8XLud3++Iuek8vlqKysZAC/R8lkEsFgEMFgEKFQSLx/++Mvem52dhYejwehUAgAUFNTs2gjymINKlVVVXwviYjorjE0ExHRipHtzZ4bohcLZV8W3uLxOABAIpGIQXpuoK6qqoJUKoVMJoNUKv3C+3f6fdn7a9asQUlJCSQSyaJl7vOCICxYMpnMgl9LJBKIx+NIJBLz7n/Rc192PxaLfaX/5Z00aigUCjH8KpVKruhORER5w9BMRES0gEQiIYa/hcJ2OBz+SsHyTr43mUwuGoTvxmJhOxvO7ybI38l9mUz2hb32FRUVKCkpWaJ3jIiIaGkwNBMRES0jCwXphcIxERER3R8MzURERERERESL4BgpIiIiIiIiokUwNBMREREREREtgqGZiIiIiIiIaBEMzURERERERESLYGgmIiIiIiIiWgRDMxEREREREdEiGJqJiIiIiIiIFsHQTESUBxKJZMHyyiuviN9jNpvnPf/yyy/n/J433ngDJpMJW7duxcWLF/P9MoiIiIhWHYkgCEKhN4KIaKWz2+05j0+cOIEjR47g5s2bWLt2LYDPQ/ORI0fwgx/8QPw+uVyOqqoqAIDVakVPTw/+/Oc/Y3p6Gr/4xS9w48aN/L0IIiIiolVoTaE3gIhoNdDpdDmP//Wvf2H//v1iYM6Sy+XzvjcrEAigtrYWXV1d0Ol0iEajS7a9RERERPQ5Ds8mIsozh8OB48eP48iRI/Oee/nll6FSqbB161a88sorSKVS4nMbN25EV1cXFAoFOjs7cfTo0XxuNhEREdGqxOHZRER59pvf/AYvv/wybDYbysvLxa+/9tpreOCBB6BUKnH+/Hm8+OKLePrpp/Haa6/l/LzH40FlZSUqKiryvelEREREqw5DMxHRffaXv/wFzzzzjPj4xIkTePjhh8XH7e3tePTRR/G73/3uC3/Pm2++iWeeeQahUAgymWzJtpeIiIiIFsfQTER0nwWDQTgcDvGx0WgUe4XPnDmDRx55BNeuXcPmzZu/8PcMDAxg48aNGBoaQltb25JuMxEREREtjAuBERHdZ3K5HHK5fMHn/vjHP6K7u/tLAzMAXLt2DSUlJdBoNPd7E4mIiIjoDjE0ExHlSSAQwN///ne8+uqr8567cOECLl68iP3790Mul+PChQt47rnn8N3vfhd1dXUF2FoiIiIiAhiaiYjy5p133oEgCHjqqafmPSeTyfDOO+/gl7/8JeLxOJqbm/Hcc8/hJz/5SQG2lIiIiIiyOKeZiIiIiIiIaBG8TjMRERERERHRIhiaiYiIiIiIiBbB0ExERERERES0CIZmIiIiIiIiokUwNBMREREREREtgqGZiIiIiIiIaBEMzURERERERESLYGgmIiIiIiIiWgRDMxEREREREdEiGJqJiIiIiIiIFsHQTERERERERLSI/wdz/zSIVn7XNwAAAABJRU5ErkJggg==">
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 14 cell-14-cell_type" class="cell markdown" data-index="14" data-loc="5" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-14-loc nb-loc-label" aria-labelledby="nb-cell-label 14" class="nb-anchor" href="#14" id="14">
14
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-14-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-14-select">
cell type
<select form="cell-14" name="cell_type">
<option id="cell-14-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-14-source-label" class="nb-toolbar" hidden id="cell-14" name="cell-14">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
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<td class="nb-end" hidden id="cell-14-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-14-source-label nb-source-label" class="nb-source" form="cell-14" id="cell-14-source" name="source" readonly rows="5">The plot above shows the positioning of TIC 2527981 relative to sector 27, camera 1, CCD 4. You can see that it is more or less on the edge of this CCD, and not the center. Note also that the `s_ra` and `s_dec` fall exactly in the center of the CCD.
From this observation we can now retrieve the corresponding TICA products (FFIs). Let's use the `get_product_list` method to retrieve these FFIs.
&lt;div class="alert alert-block alert-info"&gt;&lt;b&gt;Note:&lt;/b&gt; If you do not know which sectors contain your target, you should query for your target without the `sequence_number` filter to see what is available. However, the cutout functionality does not currently support making cross-sector cutouts, so FFI products from different sectors cannot be stacked into the same cube. Keep this in mind when retrieving the FFI products intended for your cube.&lt;/div&gt;</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>The plot above shows the positioning of TIC 2527981 relative to sector 27, camera 1, CCD 4. You can see that it is more or less on the edge of this CCD, and not the center. Note also that the <span class="sb" title="Literal.String.Backtick">`s_ra`</span> and <span class="sb" title="Literal.String.Backtick">`s_dec`</span> fall exactly in the center of the CCD.
From this observation we can now retrieve the corresponding TICA products (FFIs). Let's use the <span class="sb" title="Literal.String.Backtick">`get_product_list`</span> method to retrieve these FFIs.
&lt;div class="alert alert-block alert-info"&gt;&lt;b&gt;Note:&lt;/b&gt; If you do not know which sectors contain your target, you should query for your target without the <span class="sb" title="Literal.String.Backtick">`sequence_number`</span> filter to see what is available. However, the cutout functionality does not currently support making cross-sector cutouts, so FFI products from different sectors cannot be stacked into the same cube. Keep this in mind when retrieving the FFI products intended for your cube.&lt;/div&gt;
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-14-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-14" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-14-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-14-loc" role="none">
5
</td>
<td class="nb-outputs" role="none">
<p>
The plot above shows the positioning of TIC 2527981 relative to sector 27, camera 1, CCD 4. You can see that it is more or less on the edge of this CCD, and not the center. Note also that the
<code>
s_ra
</code>
and
<code>
s_dec
</code>
fall exactly in the center of the CCD.
</p>
<p>
From this observation we can now retrieve the corresponding TICA products (FFIs). Let's use the
<code>
get_product_list
</code>
method to retrieve these FFIs.
</p>
<div class="alert alert-block alert-info">
<b>
Note:
</b>
If you do not know which sectors contain your target, you should query for your target without the `sequence_number` filter to see what is available. However, the cutout functionality does not currently support making cross-sector cutouts, so FFI products from different sectors cannot be stacked into the same cube. Keep this in mind when retrieving the FFI products intended for your cube.
</div>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 15 cell-15-cell_type" class="cell code" data-index="15" data-loc="2" role="listitem">
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<a aria-describedby="nb-code-label nb-cell-label cell-15-loc nb-loc-label" aria-labelledby="nb-cell-label 15" class="nb-anchor" href="#15" id="15">
15
</a>
</td>
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<label class="nb-execution_count" id="cell-15-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
5
</span>
<span aria-hidden="true">
]
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<label class="nb-cell_type" id="nb-cell-15-select">
cell type
<select form="cell-15" name="cell_type">
<option value="markdown">
markdown
</option>
<option id="cell-15-cell_type" selected value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
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<form aria-labelledby="cell-15-source-label" class="nb-toolbar" hidden id="cell-15" name="cell-15">
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Run Cell
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<td class="nb-start" hidden id="cell-15-start" role="none">
<time datetime="2024-02-02T04:06:46.192+00:00">
2024-02-02T04:06:46.192758+00:00
</time>
</td>
<td class="nb-end" hidden id="cell-15-end" role="none">
<time datetime="2024-02-02T04:06:54.041+00:00">
2024-02-02T04:06:54.041111+00:00
</time>
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-15-source-label nb-source-label" class="nb-source" form="cell-15" id="cell-15-source" name="source" readonly rows="2">products = Observations.get_product_list(obs)
products</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="n" title="Name">products</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">get_product_list</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">obs</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">products</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-15-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-15" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-15-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{'scrolled': True, 'execution': {'iopub.status.busy': '2024-02-02T04:06:46.192507Z', 'iopub.execute_input': '2024-02-02T04:06:46.192758Z', 'iopub.status.idle': '2024-02-02T04:06:54.046918Z', 'shell.execute_reply': '2024-02-02T04:06:54.041111Z'}}
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2
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[
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5
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has 1 output
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</td>
<td>
--
</td>
<td>
--
</td>
<td>
--
</td>
<td>
TICA
</td>
<td>
1
</td>
<td>
N/A
</td>
<td>
hlsp_tica_tess_ffi_s0027-o2-00119975-cam1-ccd4_tess_v01_img.fits
</td>
<td>
17795520
</td>
<td>
96814766
</td>
<td>
PUBLIC
</td>
<td>
4
</td>
</tr>
<tr>
<td>
97164062
</td>
<td>
HLSP
</td>
<td>
image
</td>
<td>
hlsp_tica_tess_ffi_s0027-o2-00119976-cam1-ccd4_tess_v01_img
</td>
<td>
FITS
</td>
<td>
D
</td>
<td>
mast:HLSP/tica/s0027/cam1-ccd4/hlsp_tica_tess_ffi_s0027-o2-00119976-cam1-ccd4_tess_v01_img.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
--
</td>
<td>
--
</td>
<td>
TICA
</td>
<td>
1
</td>
<td>
N/A
</td>
<td>
hlsp_tica_tess_ffi_s0027-o2-00119976-cam1-ccd4_tess_v01_img.fits
</td>
<td>
17795520
</td>
<td>
96814766
</td>
<td>
PUBLIC
</td>
<td>
4
</td>
</tr>
<tr>
<td>
97161371
</td>
<td>
HLSP
</td>
<td>
image
</td>
<td>
hlsp_tica_tess_ffi_s0027-o2-00119977-cam1-ccd4_tess_v01_img
</td>
<td>
FITS
</td>
<td>
D
</td>
<td>
mast:HLSP/tica/s0027/cam1-ccd4/hlsp_tica_tess_ffi_s0027-o2-00119977-cam1-ccd4_tess_v01_img.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
--
</td>
<td>
--
</td>
<td>
TICA
</td>
<td>
1
</td>
<td>
N/A
</td>
<td>
hlsp_tica_tess_ffi_s0027-o2-00119977-cam1-ccd4_tess_v01_img.fits
</td>
<td>
17795520
</td>
<td>
96814766
</td>
<td>
PUBLIC
</td>
<td>
4
</td>
</tr>
</tbody>
</table>
</div>
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 16 cell-16-cell_type" class="cell markdown" data-index="16" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-16-loc nb-loc-label" aria-labelledby="nb-cell-label 16" class="nb-anchor" href="#16" id="16">
16
</a>
</td>
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<label class="nb-execution_count" id="cell-16-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
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<label class="nb-cell_type" id="nb-cell-16-select">
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</option>
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</option>
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</option>
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<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-16-source-label nb-source-label" class="nb-source" form="cell-16" id="cell-16-source" name="source" readonly rows="1">### Downloading the FFIs</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">### Downloading the FFIs</span>
</code></pre>
</div>
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<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-16-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-16" name="metadata" onclick="openDialog()">
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<pre><code>
{}
</code></pre>
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<td class="nb-loc" hidden id="cell-16-loc" role="none">
1
</td>
<td class="nb-outputs" role="none">
<h3>
<a href="#downloading-the-ffis">
Downloading the FFIs
</a>
</h3>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 17 cell-17-cell_type" class="cell markdown" data-index="17" data-loc="5" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-17-loc nb-loc-label" aria-labelledby="nb-cell-label 17" class="nb-anchor" href="#17" id="17">
17
</a>
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<label class="nb-execution_count" id="cell-17-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
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<span>
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]
</span>
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cell type
<select form="cell-17" name="cell_type">
<option id="cell-17-cell_type" selected value="markdown">
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</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
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</button>
</fieldset>
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</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-17-source-label nb-source-label" class="nb-source" form="cell-17" id="cell-17-source" name="source" readonly rows="5">Now we are ready to download the products. Use the `download_products` call to download these locally to your machine.
&lt;div class="alert alert-block alert-info"&gt;&lt;b&gt;Note:&lt;/b&gt; If you have many files, or a large download size, you should set the `curl_flag` option to `True`. This will generate a bash script that will download the products (via cURL) when executed. &lt;/div&gt;
To save space in this example, we will only download the first 5 TICA FFIs.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>Now we are ready to download the products. Use the <span class="sb" title="Literal.String.Backtick">`download_products`</span> call to download these locally to your machine.
&lt;div class="alert alert-block alert-info"&gt;&lt;b&gt;Note:&lt;/b&gt; If you have many files, or a large download size, you should set the <span class="sb" title="Literal.String.Backtick">`curl_flag`</span> option to <span class="sb" title="Literal.String.Backtick">`True`</span>. This will generate a bash script that will download the products (via cURL) when executed. &lt;/div&gt;
To save space in this example, we will only download the first 5 TICA FFIs.
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-17-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-17" name="metadata" onclick="openDialog()">
metadata
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<dialog id="cell-17-metadata">
<form>
<button formmethod="dialog">
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</button>
<pre><code>
{}
</code></pre>
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</td>
<td class="nb-loc" hidden id="cell-17-loc" role="none">
5
</td>
<td class="nb-outputs" role="none">
<p>
Now we are ready to download the products. Use the
<code>
download_products
</code>
call to download these locally to your machine.
</p>
<div class="alert alert-block alert-info">
<b>
Note:
</b>
If you have many files, or a large download size, you should set the `curl_flag` option to `True`. This will generate a bash script that will download the products (via cURL) when executed.
</div>
<p>
To save space in this example, we will only download the first 5 TICA FFIs.
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 18 cell-18-cell_type" class="cell code" data-index="18" data-loc="2" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-code-label nb-cell-label cell-18-loc nb-loc-label" aria-labelledby="nb-cell-label 18" class="nb-anchor" href="#18" id="18">
18
</a>
</td>
<td class="nb-execution_count" role="none">
<label class="nb-execution_count" id="cell-18-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
6
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-18-select">
cell type
<select form="cell-18" name="cell_type">
<option value="markdown">
markdown
</option>
<option id="cell-18-cell_type" selected value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
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<td class="nb-start" hidden id="cell-18-start" role="none">
<time datetime="2024-02-02T04:06:54.051+00:00">
2024-02-02T04:06:54.051097+00:00
</time>
</td>
<td class="nb-end" hidden id="cell-18-end" role="none">
<time datetime="2024-02-02T04:06:59.969+00:00">
2024-02-02T04:06:59.969888+00:00
</time>
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-18-source-label nb-source-label" class="nb-source" form="cell-18" id="cell-18-source" name="source" readonly rows="2">manifest = Observations.download_products(products[:5], curl_flag=False)
manifest</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="n" title="Name">manifest</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">download_products</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">products</span><span class="p" title="Punctuation">[:</span><span class="mi" title="Literal.Number.Integer">5</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">curl_flag</span><span class="o" title="Operator">=</span><span class="kc" title="Keyword.Constant">False</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">manifest</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-18-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-18" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-18-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{'execution': {'iopub.status.busy': '2024-02-02T04:06:54.050181Z', 'iopub.execute_input': '2024-02-02T04:06:54.051097Z', 'iopub.status.idle': '2024-02-02T04:06:59.970360Z', 'shell.execute_reply': '2024-02-02T04:06:59.969888Z'}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-18-loc" role="none">
2
</td>
<td class="nb-outputs" role="none">
<fieldset class="nb-outputs" data-outputs="6">
<legend aria-describedby="nb-outputs-desc" id="cell-18-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
6
</span>
<span aria-hidden="true">
]
</span>
</legend>
<span class="visually-hidden" id="cell-18-outputs-len">
has 6 outputs
</span>
<pre class="stdout">
<code>INFO: Found cached file ./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116470-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116470-cam1-ccd4_tess_v01_img.fits with expected size 17795520. [astroquery.query]
</code>
</pre>
<pre class="stdout">
<code>INFO: Found cached file ./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116471-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116471-cam1-ccd4_tess_v01_img.fits with expected size 17795520. [astroquery.query]
</code>
</pre>
<pre class="stdout">
<code>INFO: Found cached file ./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116472-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116472-cam1-ccd4_tess_v01_img.fits with expected size 17795520. [astroquery.query]
</code>
</pre>
<pre class="stdout">
<code>INFO: Found cached file ./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116473-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116473-cam1-ccd4_tess_v01_img.fits with expected size 17795520. [astroquery.query]
</code>
</pre>
<pre class="stdout">
<code>INFO: Found cached file ./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116474-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116474-cam1-ccd4_tess_v01_img.fits with expected size 17795520. [astroquery.query]
</code>
</pre>
<div>
<i>
Table length=5
</i>
<table class="table-striped table-bordered table-condensed" id="table139871843290240">
<thead>
<tr>
<th>
Local Path
</th>
<th>
Status
</th>
<th>
Message
</th>
<th>
URL
</th>
</tr>
</thead>
<thead>
<tr>
<th>
str144
</th>
<th>
str8
</th>
<th>
object
</th>
<th>
object
</th>
</tr>
</thead>
<tbody>
<tr>
<td>
./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116470-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116470-cam1-ccd4_tess_v01_img.fits
</td>
<td>
COMPLETE
</td>
<td>
None
</td>
<td>
None
</td>
</tr>
<tr>
<td>
./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116471-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116471-cam1-ccd4_tess_v01_img.fits
</td>
<td>
COMPLETE
</td>
<td>
None
</td>
<td>
None
</td>
</tr>
<tr>
<td>
./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116472-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116472-cam1-ccd4_tess_v01_img.fits
</td>
<td>
COMPLETE
</td>
<td>
None
</td>
<td>
None
</td>
</tr>
<tr>
<td>
./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116473-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116473-cam1-ccd4_tess_v01_img.fits
</td>
<td>
COMPLETE
</td>
<td>
None
</td>
<td>
None
</td>
</tr>
<tr>
<td>
./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116474-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116474-cam1-ccd4_tess_v01_img.fits
</td>
<td>
COMPLETE
</td>
<td>
None
</td>
<td>
None
</td>
</tr>
</tbody>
</table>
</div>
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 19 cell-19-cell_type" class="cell markdown" data-index="19" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-19-loc nb-loc-label" aria-labelledby="nb-cell-label 19" class="nb-anchor" href="#19" id="19">
19
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-19-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-19-select">
cell type
<select form="cell-19" name="cell_type">
<option id="cell-19-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-19-source-label" class="nb-toolbar" hidden id="cell-19" name="cell-19">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-19-start" role="none">
</td>
<td class="nb-end" hidden id="cell-19-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-19-source-label nb-source-label" class="nb-source" form="cell-19" id="cell-19-source" name="source" readonly rows="1">### Inspecting the FFIs</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">### Inspecting the FFIs</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-19-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-19" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-19-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-19-loc" role="none">
1
</td>
<td class="nb-outputs" role="none">
<h3>
<a href="#inspecting-the-ffis">
Inspecting the FFIs
</a>
</h3>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 20 cell-20-cell_type" class="cell markdown" data-index="20" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-20-loc nb-loc-label" aria-labelledby="nb-cell-label 20" class="nb-anchor" href="#20" id="20">
20
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-20-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-20-select">
cell type
<select form="cell-20" name="cell_type">
<option id="cell-20-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-20-source-label" class="nb-toolbar" hidden id="cell-20" name="cell-20">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-20-start" role="none">
</td>
<td class="nb-end" hidden id="cell-20-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-20-source-label nb-source-label" class="nb-source" form="cell-20" id="cell-20-source" name="source" readonly rows="1">Now that we have the TICA FFIs stored locally, let's do some quick plotting and inspection. We can use the `manifest` output from our download call above to get the list of our downloaded FFIs in their respective locations. Let's open up one of them and do some plotting and header inspection.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>Now that we have the TICA FFIs stored locally, let's do some quick plotting and inspection. We can use the <span class="sb" title="Literal.String.Backtick">`manifest`</span> output from our download call above to get the list of our downloaded FFIs in their respective locations. Let's open up one of them and do some plotting and header inspection.
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-20-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-20" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-20-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-20-loc" role="none">
1
</td>
<td class="nb-outputs" role="none">
<p>
Now that we have the TICA FFIs stored locally, let's do some quick plotting and inspection. We can use the
<code>
manifest
</code>
output from our download call above to get the list of our downloaded FFIs in their respective locations. Let's open up one of them and do some plotting and header inspection.
</p>
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<textarea aria-labelledby="cell-21-source-label nb-source-label" class="nb-source" form="cell-21" id="cell-21-source" name="source" readonly rows="6">ffi = manifest['Local Path'][0]
print('FFI')
print(ffi)
print(' ')
print('HDU List')
print(fits.info(ffi))</textarea>
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<div class="highlight">
<pre><span></span><code><span class="n" title="Name">ffi</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">manifest</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'Local Path'</span><span class="p" title="Punctuation">][</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">]</span>
<span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'FFI'</span><span class="p" title="Punctuation">)</span>
<span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">ffi</span><span class="p" title="Punctuation">)</span>
<span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">' '</span><span class="p" title="Punctuation">)</span>
<span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'HDU List'</span><span class="p" title="Punctuation">)</span>
<span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">fits</span><span class="o" title="Operator">.</span><span class="n" title="Name">info</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">ffi</span><span class="p" title="Punctuation">))</span>
</code></pre>
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<pre class="stdout">
<code>FFI
./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116470-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116470-cam1-ccd4_tess_v01_img.fits
HDU List
Filename: ./mastDownload/HLSP/hlsp_tica_tess_ffi_s0027-o1-00116470-cam1-ccd4_tess_v01_img/hlsp_tica_tess_ffi_s0027-o1-00116470-cam1-ccd4_tess_v01_img.fits
No. Name Ver Type Cards Dimensions Format
0 PRIMARY 1 PrimaryHDU 181 (2136, 2078) float32
1 1 BinTableHDU 20 989R x 4C [K, E, E, I]
</code>
</pre>
<pre class="stdout">
<code>None
</code>
</pre>
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<textarea aria-labelledby="cell-22-source-label nb-source-label" class="nb-source" form="cell-22" id="cell-22-source" name="source" readonly rows="1">Unlike the SPOC FFIs, the TICA FFIs have the science data stored in the Primary HDU of the FITS file. The BinTable HDU contains the list of all the reference sources used to derive the WCS solutions for the data. More information on this is available in the [TICA pipeline paper](https://ui.adsabs.harvard.edu/abs/2020RNAAS...4..251F/abstract), but let's move forward and retrieve the header information from the Primary HDU.</textarea>
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<pre><span></span><code>Unlike the SPOC FFIs, the TICA FFIs have the science data stored in the Primary HDU of the FITS file. The BinTable HDU contains the list of all the reference sources used to derive the WCS solutions for the data. More information on this is available in the [<span class="nt" title="Name.Tag">TICA pipeline paper</span>](<span class="na" title="Name.Attribute">https://ui.adsabs.harvard.edu/abs/2020RNAAS...4..251F/abstract</span>), but let's move forward and retrieve the header information from the Primary HDU.
</code></pre>
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1
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<p>
Unlike the SPOC FFIs, the TICA FFIs have the science data stored in the Primary HDU of the FITS file. The BinTable HDU contains the list of all the reference sources used to derive the WCS solutions for the data. More information on this is available in the
<a href="https://ui.adsabs.harvard.edu/abs/2020RNAAS...4..251F/abstract">
TICA pipeline paper
</a>
, but let's move forward and retrieve the header information from the Primary HDU.
</p>
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<textarea aria-labelledby="cell-23-source-label nb-source-label" class="nb-source" form="cell-23" id="cell-23-source" name="source" readonly rows="2">header = fits.getheader(ffi, 0)
header</textarea>
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<pre><span></span><code><span class="n" title="Name">header</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">fits</span><span class="o" title="Operator">.</span><span class="n" title="Name">getheader</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">ffi</span><span class="p" title="Punctuation">,</span> <span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">header</span>
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<pre class="plain">SIMPLE = T / conforms to FITS standard
BITPIX = -32 / array data type
NAXIS = 2 / number of array dimensions
NAXIS1 = 2136
NAXIS2 = 2078
EXTEND = T
CRM_N = 10 / Window for CRM min/max rejection
ORBIT_ID= 61 / Orbit ID, not a physical orbit
ACS_MODE= 'FP ' / Attitude Control System mode
SC_RA = 326.8525352094406 / Predicted RA
SC_DEC = -72.42645616046441 / Predicted Dec
SC_ROLL = -145.4939246174957 / Predicted roll
SC_QUATX= 0.55015039 / Predicted S/C quaternion x
SC_QUATY= 0.8209748100000001 / Predicted S/C quaternion y
SC_QUATZ= 0.00181107 / Predicted S/C quaternion z
SC_QUATQ= 0.15274694 / Predicted S/C quaternion q
TJD_ZERO= 2457000.0 / JD-TDB epoch for which TJD = 0
STARTTJD= 2036.2780763653 / Beginning of integration in TJD (days)
MIDTJD = 2036.281548587523 / Mid TJD of exposures
ENDTJD = 2036.285020809745 / End of integration in TJD
EXPTIME = 475.2 / Effective exposures corrected for CRM
INT_TIME= 600 / Seconds of integration
PIX_CAT = 0 / ADHU target pixel bitmask ID
REQUANT = 406 / Requant table id
DIFF_HUF= 402 / Huffman table for differenced data
PRIM_HUF= 17 / Huffman table for undifferenced data
QUAL_BIT= 0 / Quality flags
SPM = 2 / SPM number (0, 1, 2, 3)
TIME = 1278009600.25 / Beginning of integration in SCT (seconds)
CADENCE = 116470 / Cadence number since start of data collection
CRM = T / Whether or not cosmic ray were mitigated
CAMNUM = 1 / Camera Number
CCDNUM = 4 / CCD number
SCIPIXS = '[45:2092,1:2048]'
GAIN_A = 5.35 / e/ADU for CCD Sector A
GAIN_B = 5.22 / e/ADU for CCD Sector B
GAIN_C = 5.22 / e/ADU for CCD Sector C
GAIN_D = 5.18 / e/ADU for CCD Sector D
UNITS = 'electrons' / Units (ADU or electrons)
TICAVER = '0.2.1 ' / tica software version
EQUINOX = 2000.0
INSTRUME= 'TESS Photometer'
TELESCOP= 'TESS '
FILTER = 'TESS '
MJD-BEG = 59035.77807636512
MJD-END = 59035.78502080962
TESS_X = 35892849.8 / Spacecraft X coord, J2000 (km from barycenter)
TESS_Y = -134444225.35 / Spacecraft Y coord, J2000 (km from barycenter)
TESS_Z = -58297130.45 / Spacecraft Z coord, J2000 (km from barycenter)
TESS_VX = 28.87 / Spacecraft X velocity (km/s)
TESS_VY = 8.34 / Spacecraft Y velocity (km/s)
TESS_VZ = 2.82 / Spacecraft Z velocity (km/s)
CTYPE1 = 'RA---TAN-SIP'
CTYPE2 = 'DEC--TAN-SIP'
CRPIX1 = 1069.0
CRPIX2 = 1024.0
CRVAL1 = 292.5174241209214
CRVAL2 = -33.6211988458537
A_ORDER = 6
B_ORDER = 6
CD1_1 = -0.00565813598864808
CD1_2 = 0.000669378434674250
CD2_1 = -0.00080531325122481
CD2_2 = -0.00567676755627247
A_2_0 = -1.9637970999756E-05
B_2_0 = 3.10767460909240E-06
A_2_1 = 7.32371401143097E-12
B_2_1 = -2.7392701421607E-09
A_2_2 = -2.1139433429455E-13
B_2_2 = 3.60403242400561E-13
A_2_3 = 5.05538338628892E-17
B_2_3 = -2.3297854387934E-16
A_2_4 = 1.89949447840758E-19
B_2_4 = 1.29042229228673E-19
A_3_0 = -2.7399863942828E-09
B_3_0 = 3.60429053593664E-10
A_3_1 = 6.39366449796730E-14
B_3_1 = -5.8552557387428E-14
A_3_2 = -3.5389690093297E-17
B_3_2 = 7.30292498787024E-17
A_3_3 = 1.23466779779167E-19
B_3_3 = -2.0293705193387E-19
A_4_0 = -2.6511444403276E-13
B_4_0 = -5.6173992070586E-14
A_4_1 = 2.43783199567528E-16
B_4_1 = -7.4836041222166E-17
A_4_2 = -2.4059789412895E-19
B_4_2 = -1.5711565888074E-19
A_5_0 = -7.1606366121718E-17
B_5_0 = -7.2676186018506E-17
A_5_1 = 1.59831269336526E-19
B_5_1 = -4.3304302412844E-21
A_6_0 = 9.56575806442678E-20
B_6_0 = 1.11121688324243E-19
A_0_2 = -2.7801956719745E-06
B_0_2 = 1.94056702277123E-05
A_1_2 = -2.9015876539002E-09
B_1_2 = 1.97968437243935E-10
A_0_3 = 1.10841795936745E-10
B_0_3 = -2.7078326271216E-09
A_1_3 = -6.5234126583117E-14
B_1_3 = 1.54501801015873E-13
A_0_4 = -8.6018306066493E-13
B_0_4 = 2.18514270496744E-13
A_1_4 = 4.42013877809758E-18
B_1_4 = 3.45828898344294E-17
A_0_5 = 1.28565072968377E-16
B_0_5 = -3.3408251035928E-17
A_1_5 = 1.68927750535696E-19
B_1_5 = -2.3547447353103E-19
A_0_6 = 4.64922786971871E-19
B_0_6 = -1.9695105762669E-20
A_1_1 = 1.69479478985904E-05
B_1_1 = -1.7141890857418E-05
AP_2_0 = -2.2751006705172E-05
BP_2_0 = 2.79362377987846E-06
AP_2_1 = -1.4877743827504E-09
BP_2_1 = 3.76913359535467E-09
AP_2_2 = -8.1309922187360E-13
BP_2_2 = 6.31297081372046E-13
AP_2_3 = -2.6469163917945E-16
BP_2_3 = 4.86479314169723E-16
AP_2_4 = 1.46771410081327E-19
BP_2_4 = 3.29535299276951E-19
AP_3_0 = 3.97727413804247E-09
BP_3_0 = -5.5004798720985E-10
AP_3_1 = 4.06421153830584E-13
BP_3_1 = -5.6898690325350E-13
AP_3_2 = 2.17391382165645E-16
BP_3_2 = -3.2936380422172E-16
AP_3_3 = 3.72836028747677E-20
BP_3_3 = -5.0693984997721E-19
AP_4_0 = -6.3369214934208E-13
BP_4_0 = -3.9165491778513E-15
AP_4_1 = -4.3706978684432E-16
BP_4_1 = 2.46231708147139E-16
AP_4_2 = -2.1578931412182E-19
BP_4_2 = 5.32652122143994E-20
AP_5_0 = 2.28883146746969E-16
BP_5_0 = 6.00367158140259E-17
AP_5_1 = 3.64502468023446E-19
BP_5_1 = 1.23185271646469E-19
AP_6_0 = -1.4501838429957E-20
BP_6_0 = 1.12704297247493E-19
AP_0_2 = -3.0730261290980E-06
BP_0_2 = 1.71718295095941E-05
AP_1_2 = 3.84900900359166E-09
BP_1_2 = -1.5821962624406E-09
AP_0_3 = -2.7526260263153E-10
BP_0_3 = 3.41484840138066E-09
AP_1_3 = 6.66628367722612E-13
BP_1_3 = -2.2795324757589E-13
AP_0_4 = -9.7751723442325E-13
BP_0_4 = 3.83744294636667E-13
AP_1_4 = 1.52409212197719E-16
BP_1_4 = -1.5757511469293E-16
AP_0_5 = -1.2784097813171E-16
BP_0_5 = 8.81768661170718E-17
AP_1_5 = -1.2934772559444E-19
BP_1_5 = -2.6473983227041E-19
AP_0_6 = 5.08456451012281E-19
BP_0_6 = 4.32291210954868E-20
AP_1_1 = 1.52699675705178E-05
BP_1_1 = -1.9757951553926E-05
RA_TARG = 292.5174241209214
DEC_TARG= -33.6211988458537
RMSA = 3.079653262821952 / WCS fit resid all targs [arcsec]
RMSB = 3.001091070287755 / WCS fit resid bright (Tmag&lt;10) targs [arcsec]
RMSAP = 0.1526828282784404 / WCS fit resid all targs [pixel]
RMSBP = 0.1488266783979096 / WCS fit resid bright (Tmag&lt;10) targs [pixel]
RMSX0 = 2.881432843990396 / WCS fit resid extra 0 [arcsec]
RMSX1 = 3.178843366223703 / WCS fit resid extra 1 [arcsec]
RMSX2 = 2.416994518069152 / WCS fit resid extra 2 [arcsec]
RMSX3 = 2.908845430868928 / WCS fit resid extra 3 [arcsec]
WCSGDF = 0.9929221435793731 / Fraction of control point targs valid
CTRPCOL = 5 / Subregion analysis blocks over columns
CTRPROW = 5 / Subregion analysis blocks over rows
FLXWIN = 11 / Width in pixels of Flux-weight centroid region
CHECKSUM= 'aX34cV24aV24aV24' / HDU checksum updated 2020-12-30T16:35:36
DATASUM = '2085424511' / data unit checksum updated 2020-12-30T16:35:36
COMMENT calibration applied at 2020-12-30T19:21:59.245436 </pre>
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<textarea aria-labelledby="cell-24-source-label nb-source-label" class="nb-source" form="cell-24" id="cell-24-source" name="source" readonly rows="1">The header information above contains important metadata like the CD matrix elements used for the WCS transformations, exposure time of the observation, size of the image, and so on. Now let's retrieve the science data from this Primary HDU and plot the sector 27 camera 1 CCD 4 field.</textarea>
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<pre><span></span><code>The header information above contains important metadata like the CD matrix elements used for the WCS transformations, exposure time of the observation, size of the image, and so on. Now let's retrieve the science data from this Primary HDU and plot the sector 27 camera 1 CCD 4 field.
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<p>
The header information above contains important metadata like the CD matrix elements used for the WCS transformations, exposure time of the observation, size of the image, and so on. Now let's retrieve the science data from this Primary HDU and plot the sector 27 camera 1 CCD 4 field.
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<textarea aria-labelledby="cell-25-source-label nb-source-label" class="nb-source" form="cell-25" id="cell-25-source" name="source" readonly rows="14"># Retrieve the data and some header metadata for labeling
data = fits.getdata(ffi, 0)
cam, ccd = header['CAMNUM'], header['CCDNUM']
# Plotting
plt.imshow(data, vmin=0, vmax=1000000)
# Labeling
plt.title(f'Sector 27, Cam {cam}, CCD {ccd}', fontsize=20)
plt.xlabel('COLUMNS (X)', fontsize=15)
plt.ylabel('ROWS (Y)', fontsize=15)
plt.tick_params(axis='both', which='major', labelsize=15)
plt.show()</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Retrieve the data and some header metadata for labeling</span>
<span class="n" title="Name">data</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">fits</span><span class="o" title="Operator">.</span><span class="n" title="Name">getdata</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">ffi</span><span class="p" title="Punctuation">,</span> <span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">cam</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">ccd</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">header</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'CAMNUM'</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">header</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'CCDNUM'</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># Plotting</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">imshow</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">data</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">vmin</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">vmax</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">1000000</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Labeling</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">title</span><span class="p" title="Punctuation">(</span><span class="sa" title="Literal.String.Affix">f</span><span class="s1" title="Literal.String.Single">'Sector 27, Cam </span><span class="si" title="Literal.String.Interpol">{</span><span class="n" title="Name">cam</span><span class="si" title="Literal.String.Interpol">}</span><span class="s1" title="Literal.String.Single">, CCD </span><span class="si" title="Literal.String.Interpol">{</span><span class="n" title="Name">ccd</span><span class="si" title="Literal.String.Interpol">}</span><span class="s1" title="Literal.String.Single">'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">fontsize</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">20</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">xlabel</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'COLUMNS (X)'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">fontsize</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">ylabel</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'ROWS (Y)'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">fontsize</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">tick_params</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">axis</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'both'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">which</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'major'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">labelsize</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">show</span><span class="p" title="Punctuation">()</span>
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">
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</td>
</tr>
<tr aria-labelledby="nb-cell-label 26 cell-26-cell_type" class="cell markdown" data-index="26" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-26-loc nb-loc-label" aria-labelledby="nb-cell-label 26" class="nb-anchor" href="#26" id="26">
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<label class="nb-execution_count" id="cell-26-source-label">
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<label class="nb-cell_type" id="nb-cell-26-select">
cell type
<select form="cell-26" name="cell_type">
<option id="cell-26-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-26-source-label" class="nb-toolbar" hidden id="cell-26" name="cell-26">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
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<td class="nb-start" hidden id="cell-26-start" role="none">
</td>
<td class="nb-end" hidden id="cell-26-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-26-source-label nb-source-label" class="nb-source" form="cell-26" id="cell-26-source" name="source" readonly rows="1">## Creating a Cube</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## Creating a Cube</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-26-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-26" name="metadata" onclick="openDialog()">
metadata
</button>
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<button formmethod="dialog">
Close
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<pre><code>
{}
</code></pre>
</form>
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</td>
<td class="nb-loc" hidden id="cell-26-loc" role="none">
1
</td>
<td class="nb-outputs" role="none">
<h2>
<a href="#creating-a-cube">
Creating a Cube
</a>
</h2>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 27 cell-27-cell_type" class="cell markdown" data-index="27" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-27-loc nb-loc-label" aria-labelledby="nb-cell-label 27" class="nb-anchor" href="#27" id="27">
27
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cell type
<select form="cell-27" name="cell_type">
<option id="cell-27-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-27-source-label" class="nb-toolbar" hidden id="cell-27" name="cell-27">
<fieldset>
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actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-27-start" role="none">
</td>
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<textarea aria-labelledby="cell-27-source-label nb-source-label" class="nb-source" form="cell-27" id="cell-27-source" name="source" readonly rows="1">Now that we have retrieved, downloaded, and inspected our TICA FFIs, it's time to make a cube out of them. To do this, we will call on astrocut's `TicaCubeFactory` class which contains all the functionality to stack the TICA FFIs into a cube as efficiently as possible and store all the FFIs' metadata in a binary table.</textarea>
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<div class="highlight">
<pre><span></span><code>Now that we have retrieved, downloaded, and inspected our TICA FFIs, it's time to make a cube out of them. To do this, we will call on astrocut's <span class="sb" title="Literal.String.Backtick">`TicaCubeFactory`</span> class which contains all the functionality to stack the TICA FFIs into a cube as efficiently as possible and store all the FFIs' metadata in a binary table.
</code></pre>
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<p>
Now that we have retrieved, downloaded, and inspected our TICA FFIs, it's time to make a cube out of them. To do this, we will call on astrocut's
<code>
TicaCubeFactory
</code>
class which contains all the functionality to stack the TICA FFIs into a cube as efficiently as possible and store all the FFIs' metadata in a binary table.
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<textarea aria-labelledby="cell-28-source-label nb-source-label" class="nb-source" form="cell-28" id="cell-28-source" name="source" readonly rows="1">tica_cube_maker = TicaCubeFactory()</textarea>
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<pre><span></span><code><span class="n" title="Name">tica_cube_maker</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">TicaCubeFactory</span><span class="p" title="Punctuation">()</span>
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{'execution': {'iopub.status.busy': '2024-02-02T04:07:00.716083Z', 'iopub.execute_input': '2024-02-02T04:07:00.716648Z', 'iopub.status.idle': '2024-02-02T04:07:00.720866Z', 'shell.execute_reply': '2024-02-02T04:07:00.720227Z'}}
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<textarea aria-labelledby="cell-29-source-label nb-source-label" class="nb-source" form="cell-29" id="cell-29-source" name="source" readonly rows="1">We will utilize the `manifest` output from our `download_products` call in the previous section to feed `TicaCubeFactory.make_cube` the batch of 5 TICA FFIs for the cube-making process. The `verbose` argument is set to `True` by default, which will print updates about the call, but you may change `verbose` to `False` if you so choose.</textarea>
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<pre><span></span><code>We will utilize the <span class="sb" title="Literal.String.Backtick">`manifest`</span> output from our <span class="sb" title="Literal.String.Backtick">`download_products`</span> call in the previous section to feed <span class="sb" title="Literal.String.Backtick">`TicaCubeFactory.make_cube`</span> the batch of 5 TICA FFIs for the cube-making process. The <span class="sb" title="Literal.String.Backtick">`verbose`</span> argument is set to <span class="sb" title="Literal.String.Backtick">`True`</span> by default, which will print updates about the call, but you may change <span class="sb" title="Literal.String.Backtick">`verbose`</span> to <span class="sb" title="Literal.String.Backtick">`False`</span> if you so choose.
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<p>
We will utilize the
<code>
manifest
</code>
output from our
<code>
download_products
</code>
call in the previous section to feed
<code>
TicaCubeFactory.make_cube
</code>
the batch of 5 TICA FFIs for the cube-making process. The
<code>
verbose
</code>
argument is set to
<code>
True
</code>
by default, which will print updates about the call, but you may change
<code>
verbose
</code>
to
<code>
False
</code>
if you so choose.
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<textarea aria-labelledby="cell-30-source-label nb-source-label" class="nb-source" form="cell-30" id="cell-30-source" name="source" readonly rows="1">cube = tica_cube_maker.make_cube(manifest['Local Path'])</textarea>
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<pre><span></span><code><span class="n" title="Name">cube</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">tica_cube_maker</span><span class="o" title="Operator">.</span><span class="n" title="Name">make_cube</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">manifest</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'Local Path'</span><span class="p" title="Punctuation">])</span>
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<code>Using hlsp_tica_tess_ffi_s0027-o1-00116470-cam1-ccd4_tess_v01_img.fits to initialize the image header table.
Cube will be made in 1 blocks of 2079 rows each.
Completed file 0 in 0.0499 sec.
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<textarea aria-labelledby="cell-31-source-label nb-source-label" class="nb-source" form="cell-31" id="cell-31-source" name="source" readonly rows="1">The cube file is stored in your current working directory with the default name `img-cube.fits` if none is specified. Let's inspect the contents of the cube file:</textarea>
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<pre><span></span><code>The cube file is stored in your current working directory with the default name <span class="sb" title="Literal.String.Backtick">`img-cube.fits`</span> if none is specified. Let's inspect the contents of the cube file:
</code></pre>
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<p>
The cube file is stored in your current working directory with the default name
<code>
img-cube.fits
</code>
if none is specified. Let's inspect the contents of the cube file:
</p>
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<pre><span></span><code><span class="n" title="Name">fits</span><span class="o" title="Operator">.</span><span class="n" title="Name">info</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cube</span><span class="p" title="Punctuation">)</span>
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<code>Filename: img-cube.fits
No. Name Ver Type Cards Dimensions Format
0 PRIMARY 1 PrimaryHDU 182 ()
1 1 ImageHDU 9 (1, 5, 2136, 2078) float32
2 1 BinTableHDU 550 5R x 182C [J, J, J, J, J, J, J, J, 2A, D, D, D, D, D, D, D, D, D, D, D, D, J, J, J, J, J, J, J, D, J, J, J, J, 16A, D, D, D, D, 9A, 5A, D, 15A, 4A, 4A, D, D, D, D, D, D, D, D, 12A, 12A, D, D, D, D, J, J, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, D, J, J, J, 16A, 10A, 49A, 64A]
</code>
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<textarea aria-labelledby="cell-33-source-label nb-source-label" class="nb-source" form="cell-33" id="cell-33-source" name="source" readonly rows="11">The structure for each cube file is the same for both TICA and SPOC. It consists of:
* **The Primary HDU**, which contains header information derived from the first FFI in the cube stack, as well as WCS information derived from the FFI in the middle of the cube stack.
* **The Image HDU**, which is the cube itself. The dimensions of the cube for this example is `(2, 5, 2136, 2078)` and can be explained as follows:
* The 0th axis represents the science and error "layers", with the science arrays in index=0 and the error arrays in index=1. Because the TICA pipeline does not propagate errors, the error layer is empty for all the FFIs and should be ignored.
* The 1st axis represents the number of FFIs that went into the cube. For this example it is 5. Indexing this will pull out the 2D array of science data for a given FFI.
* The 2nd axis represents the number of rows each FFI consists of.
* The 3rd axis represents the number of columns each FFI consists of.
* **The BinTable HDU**, which contains the image header information for every FFI that went into the stack in the form of a table. Each column in the table is an image header keyword (for TICA it's the primary header keyword, since the TICA image lives in the primary HDU), and each row in the column is the corresponding value for that keyword for an FFI.
For more information on the cube file structure, see the [cube file format section](https://astrocut.readthedocs.io/en/latest/astrocut/file_formats.html#cube-files) of the Astrocut documentation.</textarea>
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<pre><span></span><code>The structure for each cube file is the same for both TICA and SPOC. It consists of:
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="gs" title="Generic.Strong">**The Primary HDU**</span>, which contains header information derived from the first FFI in the cube stack, as well as WCS information derived from the FFI in the middle of the cube stack.
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="gs" title="Generic.Strong">**The Image HDU**</span>, which is the cube itself. The dimensions of the cube for this example is <span class="sb" title="Literal.String.Backtick">`(2, 5, 2136, 2078)`</span> and can be explained as follows:
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>The 0th axis represents the science and error "layers", with the science arrays in index=0 and the error arrays in index=1. Because the TICA pipeline does not propagate errors, the error layer is empty for all the FFIs and should be ignored.
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>The 1st axis represents the number of FFIs that went into the cube. For this example it is 5. Indexing this will pull out the 2D array of science data for a given FFI.
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>The 2nd axis represents the number of rows each FFI consists of.
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>The 3rd axis represents the number of columns each FFI consists of.
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="gs" title="Generic.Strong">**The BinTable HDU**</span>, which contains the image header information for every FFI that went into the stack in the form of a table. Each column in the table is an image header keyword (for TICA it's the primary header keyword, since the TICA image lives in the primary HDU), and each row in the column is the corresponding value for that keyword for an FFI.
For more information on the cube file structure, see the [<span class="nt" title="Name.Tag">cube file format section</span>](<span class="na" title="Name.Attribute">https://astrocut.readthedocs.io/en/latest/astrocut/file_formats.html#cube-files</span>) of the Astrocut documentation.
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<p>
The structure for each cube file is the same for both TICA and SPOC. It consists of:
</p>
<ul>
<li>
<strong>
The Primary HDU
</strong>
, which contains header information derived from the first FFI in the cube stack, as well as WCS information derived from the FFI in the middle of the cube stack.
</li>
<li>
<strong>
The Image HDU
</strong>
, which is the cube itself. The dimensions of the cube for this example is
<code>
(2, 5, 2136, 2078)
</code>
and can be explained as follows:
<ul>
<li>
The 0th axis represents the science and error "layers", with the science arrays in index=0 and the error arrays in index=1. Because the TICA pipeline does not propagate errors, the error layer is empty for all the FFIs and should be ignored.
</li>
<li>
The 1st axis represents the number of FFIs that went into the cube. For this example it is 5. Indexing this will pull out the 2D array of science data for a given FFI.
</li>
<li>
The 2nd axis represents the number of rows each FFI consists of.
</li>
<li>
The 3rd axis represents the number of columns each FFI consists of.
</li>
</ul>
</li>
<li>
<strong>
The BinTable HDU
</strong>
, which contains the image header information for every FFI that went into the stack in the form of a table. Each column in the table is an image header keyword (for TICA it's the primary header keyword, since the TICA image lives in the primary HDU), and each row in the column is the corresponding value for that keyword for an FFI.
</li>
</ul>
<p>
For more information on the cube file structure, see the
<a href="https://astrocut.readthedocs.io/en/latest/astrocut/file_formats.html#cube-files">
cube file format section
</a>
of the Astrocut documentation.
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<textarea aria-labelledby="cell-34-source-label nb-source-label" class="nb-source" form="cell-34" id="cell-34-source" name="source" readonly rows="1">Before we move onto generating the cutouts, it's important to note that `CubeFactory` is distinct from `TicaCubeFactory`. Both classes are used to generate cubes, but `TicaCubeFactory` is specifically designed to generate TICA cubes, and `CubeFactory` is specifically designed to generate SPOC cubes. Any attempt at generating TICA cubes with `CubeFactory` or vice versa will be unsuccessful. See below for an example:</textarea>
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<pre><span></span><code>Before we move onto generating the cutouts, it's important to note that <span class="sb" title="Literal.String.Backtick">`CubeFactory`</span> is distinct from <span class="sb" title="Literal.String.Backtick">`TicaCubeFactory`</span>. Both classes are used to generate cubes, but <span class="sb" title="Literal.String.Backtick">`TicaCubeFactory`</span> is specifically designed to generate TICA cubes, and <span class="sb" title="Literal.String.Backtick">`CubeFactory`</span> is specifically designed to generate SPOC cubes. Any attempt at generating TICA cubes with <span class="sb" title="Literal.String.Backtick">`CubeFactory`</span> or vice versa will be unsuccessful. See below for an example:
</code></pre>
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<p>
Before we move onto generating the cutouts, it's important to note that
<code>
CubeFactory
</code>
is distinct from
<code>
TicaCubeFactory
</code>
. Both classes are used to generate cubes, but
<code>
TicaCubeFactory
</code>
is specifically designed to generate TICA cubes, and
<code>
CubeFactory
</code>
is specifically designed to generate SPOC cubes. Any attempt at generating TICA cubes with
<code>
CubeFactory
</code>
or vice versa will be unsuccessful. See below for an example:
</p>
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<textarea aria-labelledby="cell-35-source-label nb-source-label" class="nb-source" form="cell-35" id="cell-35-source" name="source" readonly rows="4"># This cell will give an error if you uncomment the lines below!
#spoc_cube_maker = CubeFactory()
#spoc_cube_maker.make_cube(manifest['Local Path'])</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># This cell will give an error if you uncomment the lines below!</span>
<span class="c1" title="Comment.Single">#spoc_cube_maker = CubeFactory()</span>
<span class="c1" title="Comment.Single">#spoc_cube_maker.make_cube(manifest['Local Path'])</span>
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<textarea aria-labelledby="cell-36-source-label nb-source-label" class="nb-source" form="cell-36" id="cell-36-source" name="source" readonly rows="1">## Creating the Cutouts</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Creating the Cutouts</span>
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<h2>
<a href="#creating-the-cutouts">
Creating the Cutouts
</a>
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<textarea aria-labelledby="cell-37-source-label nb-source-label" class="nb-source" form="cell-37" id="cell-37-source" name="source" readonly rows="3">Now that we have our cube we are able to generate cutouts of our region of interest. Unlike in the cube-making process, we have a single class for generating the cutouts from both SPOC or TICA cubes, `CutoutFactory`. To make the cutouts, we will call upon `CutoutFactory`'s `cube_cut` and feed it the cube we just made, the coordinates for the center of our region of interest, which in this case is our object TIC 2527981, the cutout size for our region (in pixels), and lastly, the product type that was used for making our cube.
&lt;div class="alert alert-block alert-info"&gt;&lt;b&gt;Note:&lt;/b&gt; The call below may generate a "VerifyWarning: Card is too long, comment will be truncated." warning message, which can safely be ignored. &lt;/div&gt;</textarea>
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<pre><span></span><code>Now that we have our cube we are able to generate cutouts of our region of interest. Unlike in the cube-making process, we have a single class for generating the cutouts from both SPOC or TICA cubes, <span class="sb" title="Literal.String.Backtick">`CutoutFactory`</span>. To make the cutouts, we will call upon <span class="sb" title="Literal.String.Backtick">`CutoutFactory`</span>'s <span class="sb" title="Literal.String.Backtick">`cube_cut`</span> and feed it the cube we just made, the coordinates for the center of our region of interest, which in this case is our object TIC 2527981, the cutout size for our region (in pixels), and lastly, the product type that was used for making our cube.
&lt;div class="alert alert-block alert-info"&gt;&lt;b&gt;Note:&lt;/b&gt; The call below may generate a "VerifyWarning: Card is too long, comment will be truncated." warning message, which can safely be ignored. &lt;/div&gt;
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<p>
Now that we have our cube we are able to generate cutouts of our region of interest. Unlike in the cube-making process, we have a single class for generating the cutouts from both SPOC or TICA cubes,
<code>
CutoutFactory
</code>
. To make the cutouts, we will call upon
<code>
CutoutFactory
</code>
's
<code>
cube_cut
</code>
and feed it the cube we just made, the coordinates for the center of our region of interest, which in this case is our object TIC 2527981, the cutout size for our region (in pixels), and lastly, the product type that was used for making our cube.
</p>
<div class="alert alert-block alert-info">
<b>
Note:
</b>
The call below may generate a "VerifyWarning: Card is too long, comment will be truncated." warning message, which can safely be ignored.
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<textarea aria-labelledby="cell-38-source-label nb-source-label" class="nb-source" form="cell-38" id="cell-38-source" name="source" readonly rows="4">cutout_maker = CutoutFactory()
cutout_file = cutout_maker.cube_cut(cube, coordinates=coordinates, cutout_size=25, product='TICA')
cutout_file</textarea>
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<pre><span></span><code><span class="n" title="Name">cutout_maker</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">CutoutFactory</span><span class="p" title="Punctuation">()</span>
<span class="n" title="Name">cutout_file</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">cutout_maker</span><span class="o" title="Operator">.</span><span class="n" title="Name">cube_cut</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cube</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">coordinates</span><span class="o" title="Operator">=</span><span class="n" title="Name">coordinates</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">cutout_size</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">25</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">product</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'TICA'</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">cutout_file</span>
</code></pre>
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<code>WARNING: VerifyWarning: Card is too long, comment will be truncated. [astropy.io.fits.card]
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<pre class="plain">'./img_289.097900_-29.337000_25x25_astrocut.fits'</pre>
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<textarea aria-labelledby="cell-39-source-label nb-source-label" class="nb-source" form="cell-39" id="cell-39-source" name="source" readonly rows="4">Our cutout file is saved to our current working directory with the default name structured as follows:
`img_[RA]_[Dec]_[rows]x[cols]_astrocut.fits`
Let's inspect the HDU list of this file...</textarea>
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<pre><span></span><code>Our cutout file is saved to our current working directory with the default name structured as follows:
<span class="sb" title="Literal.String.Backtick">`img_[RA]_[Dec]_[rows]x[cols]_astrocut.fits`</span>
Let's inspect the HDU list of this file...
</code></pre>
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<p>
Our cutout file is saved to our current working directory with the default name structured as follows:
<code>
img_[RA]_[Dec]_[rows]x[cols]_astrocut.fits
</code>
</p>
<p>
Let's inspect the HDU list of this file...
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<pre><span></span><code><span class="n" title="Name">fits</span><span class="o" title="Operator">.</span><span class="n" title="Name">info</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cutout_file</span><span class="p" title="Punctuation">)</span>
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<code>Filename: ./img_289.097900_-29.337000_25x25_astrocut.fits
No. Name Ver Type Cards Dimensions Format
0 PRIMARY 1 PrimaryHDU 57 ()
1 PIXELS 1 BinTableHDU 277 5R x 11C [D, J, 625J, 625E, 625E, 625E, 625E, J, E, E, 38A]
2 APERTURE 1 ImageHDU 82 (25, 25) int32
</code>
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<textarea aria-labelledby="cell-41-source-label nb-source-label" class="nb-source" form="cell-41" id="cell-41-source" name="source" readonly rows="6">The structure for each cutout file is the same for both TICA and SPOC. It consists of:
* **The Primary HDU**, which contains header information derived from the first FFI in the cube stack, as well as WCS information derived from the FFI in the middle of the cube stack.
* **The BinTable HDU**, which is a FITS table that stores the actual cutouts under the `FLUX` column, with each row being occupied by a single FFI cutout of the requested size.
* **The Image HDU**, which contains the aperture, or field of view, of the cutouts. This image is only useful for the moving targets feature on TESSCut, and should be blank if your target has a set of coordinates assigned to it.
For more information on the cutout file structure, see the [target pixel file format section](https://astrocut.readthedocs.io/en/latest/astrocut/file_formats.html#target-pixel-files) of the Astrocut documentation. For now let's take a closer look at the Primary HDU header:</textarea>
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<pre><span></span><code>The structure for each cutout file is the same for both TICA and SPOC. It consists of:
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="gs" title="Generic.Strong">**The Primary HDU**</span>, which contains header information derived from the first FFI in the cube stack, as well as WCS information derived from the FFI in the middle of the cube stack.
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="gs" title="Generic.Strong">**The BinTable HDU**</span>, which is a FITS table that stores the actual cutouts under the <span class="sb" title="Literal.String.Backtick">`FLUX`</span> column, with each row being occupied by a single FFI cutout of the requested size.
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="gs" title="Generic.Strong">**The Image HDU**</span>, which contains the aperture, or field of view, of the cutouts. This image is only useful for the moving targets feature on TESSCut, and should be blank if your target has a set of coordinates assigned to it.
For more information on the cutout file structure, see the [<span class="nt" title="Name.Tag">target pixel file format section</span>](<span class="na" title="Name.Attribute">https://astrocut.readthedocs.io/en/latest/astrocut/file_formats.html#target-pixel-files</span>) of the Astrocut documentation. For now let's take a closer look at the Primary HDU header:
</code></pre>
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<p>
The structure for each cutout file is the same for both TICA and SPOC. It consists of:
</p>
<ul>
<li>
<strong>
The Primary HDU
</strong>
, which contains header information derived from the first FFI in the cube stack, as well as WCS information derived from the FFI in the middle of the cube stack.
</li>
<li>
<strong>
The BinTable HDU
</strong>
, which is a FITS table that stores the actual cutouts under the
<code>
FLUX
</code>
column, with each row being occupied by a single FFI cutout of the requested size.
</li>
<li>
<strong>
The Image HDU
</strong>
, which contains the aperture, or field of view, of the cutouts. This image is only useful for the moving targets feature on TESSCut, and should be blank if your target has a set of coordinates assigned to it.
</li>
</ul>
<p>
For more information on the cutout file structure, see the
<a href="https://astrocut.readthedocs.io/en/latest/astrocut/file_formats.html#target-pixel-files">
target pixel file format section
</a>
of the Astrocut documentation. For now let's take a closer look at the Primary HDU header:
</p>
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<textarea aria-labelledby="cell-42-source-label nb-source-label" class="nb-source" form="cell-42" id="cell-42-source" name="source" readonly rows="2">cutout_header = fits.getheader(cutout_file, 0)
cutout_header</textarea>
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<pre><span></span><code><span class="n" title="Name">cutout_header</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">fits</span><span class="o" title="Operator">.</span><span class="n" title="Name">getheader</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cutout_file</span><span class="p" title="Punctuation">,</span> <span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">cutout_header</span>
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<pre class="plain">SIMPLE = T / conforms to FITS standard
BITPIX = 8 / array data type
NAXIS = 0 / number of array dimensions
EXTEND = T
TICAVER = '0.2.1 ' / tica software version
EQUINOX = 2000.0
INSTRUME= 'TESS Photometer'
TELESCOP= 'TESS '
CHECKSUM= '0ocj0lch0lch0lch' / HDU checksum updated 2024-02-01T20:07:01
DATASUM = '0 ' / data unit checksum updated 2024-02-01T20:07:01
ORIGIN = 'STScI/MAST'
DATE = '2024-02-01' / FFI cube creation date
SECTOR = / Observing sector
EXTNAME = 'PRIMARY '
CREATOR = 'astrocut' / software used to produce this file
PROCVER = '0.10.0 ' / software version
FFI_TYPE= 'TICA ' / the FFI type used to make the cutouts
RA_OBJ = 289.0979 / [deg] right ascension
DEC_OBJ = -29.337 / [deg] declination
TIMEREF = / barycentric correction applied to times
TASSIGN = / where time is assigned
TIMESYS = 'TDB ' / time system is Barycentric Dynamical Time (TDB)
BJDREFI = 2457000 / integer part of BTJD reference date
BJDREFF = 0.0 / fraction of the day in BTJD reference date
TIMEUNIT= 'd ' / time unit for TIME, TSTART and TSTOP
EXTVER = '1 ' / extension version number (not format version)
SIMDATA = F / file is based on simulated data
NEXTEND = '2 ' / number of standard extensions
TSTART = 2036.2780763653 / observation start time in TJD of first FFI
TSTOP = 2036.312798574792 / observation stop time in TJD of last FFI
CAMERA = 1 / Camera number
CCD = 4 / CCD chip number
ASTATE = / archive state F indicates single orbit processing
CRMITEN = T / spacecraft cosmic ray mitigation enabled
CRBLKSZ = / [exposures] s/c cosmic ray mitigation block siz
FFIINDEX= 116470 / number of FFI cadence interval of first FFI
DATA_REL= / data release version number
DATE-OBS= '2020-07-05 18:40:25.798' / TSTART as UTC calendar date of first FFI
DATE-END= '2020-07-05 19:30:25.797' / TSTOP as UTC calendar date of last FFI
FILEVER = / file format version
RADESYS = / reference frame of celestial coordinates
SCCONFIG= / spacecraft configuration ID
TIMVERSN= / OGIP memo number for file format
TELAPSE = 0.034722209491974354 / [d] TSTOP - TSTART
OBJECT = '' / string version of target id
TCID = 0 / unique tess target identifier
PXTABLE = 0 / pixel table id
PMRA = 0.0 / [mas/yr] RA proper motion
PMDEC = 0.0 / [mas/yr] Dec proper motion
PMTOTAL = 0.0 / [mas/yr] total proper motion
TESSMAG = 0.0 / [mag] TESS magnitude
TEFF = 0.0 / [K] Effective temperature
LOGG = 0.0 / [cm/s2] log10 surface gravity
MH = 0.0 / [log10([M/H])] metallicity
RADIUS = 0.0 / [solar radii] stellar radius
TICVER = 0 / TICVER
TICID = / unique tess target identifier </pre>
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<textarea aria-labelledby="cell-43-source-label nb-source-label" class="nb-source" form="cell-43" id="cell-43-source" name="source" readonly rows="1">As mentioned previously, the cutout files will have header information that is inherited from the first FFI and the middle FFI in the cube stack (just like the cube files), so most of these keywords should look familiar. However, there are some new header keywords in the cutout files that are added after processing:</textarea>
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<pre><span></span><code>As mentioned previously, the cutout files will have header information that is inherited from the first FFI and the middle FFI in the cube stack (just like the cube files), so most of these keywords should look familiar. However, there are some new header keywords in the cutout files that are added after processing:
</code></pre>
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<p>
As mentioned previously, the cutout files will have header information that is inherited from the first FFI and the middle FFI in the cube stack (just like the cube files), so most of these keywords should look familiar. However, there are some new header keywords in the cutout files that are added after processing:
</p>
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<textarea aria-labelledby="cell-44-source-label nb-source-label" class="nb-source" form="cell-44" id="cell-44-source" name="source" readonly rows="2">print(cutout_header.cards['FFI_TYPE'])
print(cutout_header.cards['PROCVER'])</textarea>
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<pre><span></span><code><span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cutout_header</span><span class="o" title="Operator">.</span><span class="n" title="Name">cards</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'FFI_TYPE'</span><span class="p" title="Punctuation">])</span>
<span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cutout_header</span><span class="o" title="Operator">.</span><span class="n" title="Name">cards</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'PROCVER'</span><span class="p" title="Punctuation">])</span>
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{'execution': {'iopub.status.busy': '2024-02-02T04:07:01.615858Z', 'iopub.execute_input': '2024-02-02T04:07:01.616064Z', 'iopub.status.idle': '2024-02-02T04:07:01.618537Z', 'shell.execute_reply': '2024-02-02T04:07:01.618159Z'}}
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<code>FFI_TYPE= 'TICA ' / the FFI type used to make the cutouts
PROCVER = '0.10.0 ' / software version
</code>
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<textarea aria-labelledby="cell-45-source-label nb-source-label" class="nb-source" form="cell-45" id="cell-45-source" name="source" readonly rows="1">These keywords are intentionally added so that the cutout is formatted like a TESS target pixel file (TPF). Conveniently, this means you can use existing scripts on these cutout files without having to readapt them. Let's go ahead and plot one of the cutouts in our file:</textarea>
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<pre><span></span><code>These keywords are intentionally added so that the cutout is formatted like a TESS target pixel file (TPF). Conveniently, this means you can use existing scripts on these cutout files without having to readapt them. Let's go ahead and plot one of the cutouts in our file:
</code></pre>
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<p>
These keywords are intentionally added so that the cutout is formatted like a TESS target pixel file (TPF). Conveniently, this means you can use existing scripts on these cutout files without having to readapt them. Let's go ahead and plot one of the cutouts in our file:
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<textarea aria-labelledby="cell-46-source-label nb-source-label" class="nb-source" form="cell-46" id="cell-46-source" name="source" readonly rows="12"># Extracting the cutouts science data from the BinTable HDU
cutouts = fits.getdata(cutout_file)['FLUX']
# Plotting an arbitrary cutout from the FLUX column
plt.imshow(cutouts[3])
# Labeling
plt.xlabel('COLUMNS (X)', fontsize=15)
plt.ylabel('ROWS (Y)', fontsize=15)
plt.tick_params(axis='both', which='major', labelsize=15)
plt.show()</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Extracting the cutouts science data from the BinTable HDU</span>
<span class="n" title="Name">cutouts</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">fits</span><span class="o" title="Operator">.</span><span class="n" title="Name">getdata</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cutout_file</span><span class="p" title="Punctuation">)[</span><span class="s1" title="Literal.String.Single">'FLUX'</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># Plotting an arbitrary cutout from the FLUX column</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">imshow</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cutouts</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">3</span><span class="p" title="Punctuation">])</span>
<span class="c1" title="Comment.Single"># Labeling</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">xlabel</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'COLUMNS (X)'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">fontsize</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">ylabel</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'ROWS (Y)'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">fontsize</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">tick_params</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">axis</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'both'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">which</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'major'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">labelsize</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">show</span><span class="p" title="Punctuation">()</span>
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{'execution': {'iopub.status.busy': '2024-02-02T04:07:01.620185Z', 'iopub.execute_input': '2024-02-02T04:07:01.620436Z', 'shell.execute_reply': '2024-02-02T04:07:01.758695Z', 'iopub.status.idle': '2024-02-02T04:07:01.759270Z'}}
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</span>
<img 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">
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 47 cell-47-cell_type" class="cell markdown" data-index="47" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
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<textarea aria-labelledby="cell-47-source-label nb-source-label" class="nb-source" form="cell-47" id="cell-47-source" name="source" readonly rows="1">## Exercise: Generate Cutouts for TIC 2527981 Sector 27 SPOC Products</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Exercise: Generate Cutouts for TIC 2527981 Sector 27 SPOC Products</span>
</code></pre>
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<h2>
<a href="#exercise-generate-cutouts-for-tic-2527981-sector-27-spoc-products">
Exercise: Generate Cutouts for TIC 2527981 Sector 27 SPOC Products
</a>
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<textarea aria-labelledby="cell-48-source-label nb-source-label" class="nb-source" form="cell-48" id="cell-48-source" name="source" readonly rows="3">Let's now generate cutouts of our target from the SPOC products of the same observation, and use this as an opportunity to compare the FFI file structure differences between SPOC and TICA.
Perform the same `astroquery.mast.Observations` query, inspect the FFIs, generate the cube (remember that there are two cube-generating classes, `CubeFactory` and `TicaCubeFactory`), and generate the cutouts from the cube, in the cells below.</textarea>
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<pre><span></span><code>Let's now generate cutouts of our target from the SPOC products of the same observation, and use this as an opportunity to compare the FFI file structure differences between SPOC and TICA.
Perform the same <span class="sb" title="Literal.String.Backtick">`astroquery.mast.Observations`</span> query, inspect the FFIs, generate the cube (remember that there are two cube-generating classes, <span class="sb" title="Literal.String.Backtick">`CubeFactory`</span> and <span class="sb" title="Literal.String.Backtick">`TicaCubeFactory`</span>), and generate the cutouts from the cube, in the cells below.
</code></pre>
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<p>
Let's now generate cutouts of our target from the SPOC products of the same observation, and use this as an opportunity to compare the FFI file structure differences between SPOC and TICA.
</p>
<p>
Perform the same
<code>
astroquery.mast.Observations
</code>
query, inspect the FFIs, generate the cube (remember that there are two cube-generating classes,
<code>
CubeFactory
</code>
and
<code>
TicaCubeFactory
</code>
), and generate the cutouts from the cube, in the cells below.
</p>
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<textarea aria-labelledby="cell-49-source-label nb-source-label" class="nb-source" form="cell-49" id="cell-49-source" name="source" readonly rows="6"># Make a query for the correct observation using `astroquery.mast.Observations.query_criteria`
# Retrieve the products corresponding to this observations using `astroquery.mast.Observations.download_products`
# Inspect one of the FFIs with `matplotlib.pyplot`. Compare the HDU List between the SPOC FFI and one of the TICA FFIs
# and note the structural differences.</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Make a query for the correct observation using `astroquery.mast.Observations.query_criteria`</span>
<span class="c1" title="Comment.Single"># Retrieve the products corresponding to this observations using `astroquery.mast.Observations.download_products`</span>
<span class="c1" title="Comment.Single"># Inspect one of the FFIs with `matplotlib.pyplot`. Compare the HDU List between the SPOC FFI and one of the TICA FFIs</span>
<span class="c1" title="Comment.Single"># and note the structural differences.</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:07:01.763532Z', 'iopub.execute_input': '2024-02-02T04:07:01.764056Z', 'shell.execute_reply': '2024-02-02T04:07:01.768451Z', 'iopub.status.idle': '2024-02-02T04:07:01.769313Z'}}
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<textarea aria-labelledby="cell-50-source-label nb-source-label" class="nb-source" form="cell-50" id="cell-50-source" name="source" readonly rows="3"># Generate a cube with the SPOC FFIs.
# If you've stored the output from the `astroquery.mast.Observations.download_products` call above,
# it's helpful to use the Local Path column as the CubeFactory input.</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Generate a cube with the SPOC FFIs.</span>
<span class="c1" title="Comment.Single"># If you've stored the output from the `astroquery.mast.Observations.download_products` call above, </span>
<span class="c1" title="Comment.Single"># it's helpful to use the Local Path column as the CubeFactory input.</span>
</code></pre>
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<textarea aria-labelledby="cell-51-source-label nb-source-label" class="nb-source" form="cell-51" id="cell-51-source" name="source" readonly rows="1"># Inspect the cube as you wish. Make a note of the cube size and ensure that the dimensions are as you expect.</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Inspect the cube as you wish. Make a note of the cube size and ensure that the dimensions are as you expect.</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:07:01.780670Z', 'iopub.execute_input': '2024-02-02T04:07:01.785739Z', 'iopub.status.idle': '2024-02-02T04:07:01.793579Z', 'shell.execute_reply': '2024-02-02T04:07:01.793012Z'}}
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<textarea aria-labelledby="cell-52-source-label nb-source-label" class="nb-source" form="cell-52" id="cell-52-source" name="source" readonly rows="1"># Generate the cutouts with the SPOC cube from above.</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Generate the cutouts with the SPOC cube from above.</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:07:01.796975Z', 'iopub.execute_input': '2024-02-02T04:07:01.797377Z', 'shell.execute_reply': '2024-02-02T04:07:01.799629Z', 'iopub.status.idle': '2024-02-02T04:07:01.800138Z'}}
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<textarea aria-labelledby="cell-53-source-label nb-source-label" class="nb-source" form="cell-53" id="cell-53-source" name="source" readonly rows="1"># Inspect the cutouts as you wish.</textarea>
<div aria-labelledby="nb-source-label" role="group">
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<pre><span></span><code><span class="c1" title="Comment.Single"># Inspect the cutouts as you wish.</span>
</code></pre>
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</button>
<pre><code>
{'execution': {'iopub.status.busy': '2024-02-02T04:07:01.803173Z', 'iopub.execute_input': '2024-02-02T04:07:01.803381Z', 'iopub.status.idle': '2024-02-02T04:07:01.805856Z', 'shell.execute_reply': '2024-02-02T04:07:01.805335Z'}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-53-loc" role="none">
1
</td>
<td class="nb-outputs" role="none">
<span class="nb-outputs visually-hidden" id="cell-53-outputs-len">
In 23 has
0
outputs.
</span>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 54 cell-54-cell_type" class="cell markdown" data-index="54" data-loc="13" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-54-loc nb-loc-label" aria-labelledby="nb-cell-label 54" class="nb-anchor" href="#54" id="54">
54
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-54-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-54-select">
cell type
<select form="cell-54" name="cell_type">
<option id="cell-54-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-54-source-label" class="nb-toolbar" hidden id="cell-54" name="cell-54">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-54-start" role="none">
</td>
<td class="nb-end" hidden id="cell-54-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-54-source-label nb-source-label" class="nb-source" form="cell-54" id="cell-54-source" name="source" readonly rows="13">## Resources
The following is a list of resources that were referenced throughout the tutorial, as well as some additional references that you may find useful:
- [astrocut](https://astrocut.readthedocs.io/en/latest/astrocut/index.html) Documentation
- [astrocut](https://github.com/spacetelescope/astrocut) GitHub repository
- [astroquery.mast](https://astroquery.readthedocs.io/en/latest/mast/mast.html) Documentation
- The [TESSCut Service](https://mast.stsci.edu/tesscut/)
- The [TESSCut API Documentation](https://mast.stsci.edu/tesscut/docs/index.html)
- More information on the [TESS mission](https://archive.stsci.edu/missions-and-data/tess)
- More information on the [TICA HLSP](https://archive.stsci.edu/hlsp/tica)
- Publication on the [SPOC Pipeline](https://ui.adsabs.harvard.edu/abs/2016SPIE.9913E..3EJ/abstract)
- Publication on the [TICA Quicklook Pipeline](https://iopscience.iop.org/article/10.3847/2515-5172/abd63a)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## Resources</span>
The following is a list of resources that were referenced throughout the tutorial, as well as some additional references that you may find useful:
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">astrocut</span>](<span class="na" title="Name.Attribute">https://astrocut.readthedocs.io/en/latest/astrocut/index.html</span>) Documentation
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">astrocut</span>](<span class="na" title="Name.Attribute">https://github.com/spacetelescope/astrocut</span>) GitHub repository
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">astroquery.mast</span>](<span class="na" title="Name.Attribute">https://astroquery.readthedocs.io/en/latest/mast/mast.html</span>) Documentation
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>The [<span class="nt" title="Name.Tag">TESSCut Service</span>](<span class="na" title="Name.Attribute">https://mast.stsci.edu/tesscut/</span>)
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>The [<span class="nt" title="Name.Tag">TESSCut API Documentation</span>](<span class="na" title="Name.Attribute">https://mast.stsci.edu/tesscut/docs/index.html</span>)
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>More information on the [<span class="nt" title="Name.Tag">TESS mission</span>](<span class="na" title="Name.Attribute">https://archive.stsci.edu/missions-and-data/tess</span>)
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>More information on the [<span class="nt" title="Name.Tag">TICA HLSP</span>](<span class="na" title="Name.Attribute">https://archive.stsci.edu/hlsp/tica</span>)
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>Publication on the [<span class="nt" title="Name.Tag">SPOC Pipeline</span>](<span class="na" title="Name.Attribute">https://ui.adsabs.harvard.edu/abs/2016SPIE.9913E..3EJ/abstract</span>)
<span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>Publication on the [<span class="nt" title="Name.Tag">TICA Quicklook Pipeline</span>](<span class="na" title="Name.Attribute">https://iopscience.iop.org/article/10.3847/2515-5172/abd63a</span>)
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-54-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-54" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-54-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-54-loc" role="none">
13
</td>
<td class="nb-outputs" role="none">
<h2>
<a href="#resources">
Resources
</a>
</h2>
<p>
The following is a list of resources that were referenced throughout the tutorial, as well as some additional references that you may find useful:
</p>
<ul>
<li>
<a href="https://astrocut.readthedocs.io/en/latest/astrocut/index.html">
astrocut
</a>
Documentation
</li>
<li>
<a href="https://github.com/spacetelescope/astrocut">
astrocut
</a>
GitHub repository
</li>
<li>
<a href="https://astroquery.readthedocs.io/en/latest/mast/mast.html">
astroquery.mast
</a>
Documentation
</li>
<li>
The
<a href="https://mast.stsci.edu/tesscut/">
TESSCut Service
</a>
</li>
<li>
The
<a href="https://mast.stsci.edu/tesscut/docs/index.html">
TESSCut API Documentation
</a>
</li>
<li>
More information on the
<a href="https://archive.stsci.edu/missions-and-data/tess">
TESS mission
</a>
</li>
<li>
More information on the
<a href="https://archive.stsci.edu/hlsp/tica">
TICA HLSP
</a>
</li>
<li>
Publication on the
<a href="https://ui.adsabs.harvard.edu/abs/2016SPIE.9913E..3EJ/abstract">
SPOC Pipeline
</a>
</li>
<li>
Publication on the
<a href="https://iopscience.iop.org/article/10.3847/2515-5172/abd63a">
TICA Quicklook Pipeline
</a>
</li>
</ul>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 55 cell-55-cell_type" class="cell markdown" data-index="55" data-loc="7" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-55-loc nb-loc-label" aria-labelledby="nb-cell-label 55" class="nb-anchor" href="#55" id="55">
55
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-55-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-55-select">
cell type
<select form="cell-55" name="cell_type">
<option id="cell-55-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-55-source-label" class="nb-toolbar" hidden id="cell-55" name="cell-55">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-55-start" role="none">
</td>
<td class="nb-end" hidden id="cell-55-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-55-source-label nb-source-label" class="nb-source" form="cell-55" id="cell-55-source" name="source" readonly rows="7">## Citations
If you use any of astroquery's tools or astrocut for published research, please cite the
authors. Follow these links for more information about citing astroquery and astrocut:
* [Citing `astroquery`](https://github.com/astropy/astroquery/blob/main/astroquery/CITATION)
* [Citing `astrocut`](https://ui.adsabs.harvard.edu/abs/2019ascl.soft05007B/abstract)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## Citations</span>
If you use any of astroquery's tools or astrocut for published research, please cite the
authors. Follow these links for more information about citing astroquery and astrocut:
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Citing `astroquery`</span>](<span class="na" title="Name.Attribute">https://github.com/astropy/astroquery/blob/main/astroquery/CITATION</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Citing `astrocut`</span>](<span class="na" title="Name.Attribute">https://ui.adsabs.harvard.edu/abs/2019ascl.soft05007B/abstract</span>)
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-55-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-55" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-55-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-55-loc" role="none">
7
</td>
<td class="nb-outputs" role="none">
<h2>
<a href="#citations">
Citations
</a>
</h2>
<p>
If you use any of astroquery's tools or astrocut for published research, please cite the
authors. Follow these links for more information about citing astroquery and astrocut:
</p>
<ul>
<li>
<a href="https://github.com/astropy/astroquery/blob/main/astroquery/CITATION">
Citing
<code>
astroquery
</code>
</a>
</li>
<li>
<a href="https://ui.adsabs.harvard.edu/abs/2019ascl.soft05007B/abstract">
Citing
<code>
astrocut
</code>
</a>
</li>
</ul>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 56 cell-56-cell_type" class="cell markdown" data-index="56" data-loc="9" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-56-loc nb-loc-label" aria-labelledby="nb-cell-label 56" class="nb-anchor" href="#56" id="56">
56
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-56-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-56-select">
cell type
<select form="cell-56" name="cell_type">
<option id="cell-56-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-56-source-label" class="nb-toolbar" hidden id="cell-56" name="cell-56">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-56-start" role="none">
</td>
<td class="nb-end" hidden id="cell-56-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-56-source-label nb-source-label" class="nb-source" form="cell-56" id="cell-56-source" name="source" readonly rows="9">## About this Notebook
If you have comments or questions on this notebook, please contact us through the Archive Help Desk e-mail at `archive@stsci.edu`.
**Author(s):** Jenny V. Medina &lt;br&gt;
**Keyword(s):** Tutorial, TESS, TICA, SPOC, astroquery, astrocut, cutouts, ffi, tpf, tesscut &lt;br&gt;
**Last Updated:** Mar 2023 &lt;br&gt;
***
[Top of Page](#top)
&lt;img style="float: right;" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" alt="Space Telescope Logo" width="200px"/&gt; </textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## About this Notebook</span>
If you have comments or questions on this notebook, please contact us through the Archive Help Desk e-mail at <span class="sb" title="Literal.String.Backtick">`archive@stsci.edu`</span>.
<span class="gs" title="Generic.Strong">**Author(s):**</span> Jenny V. Medina &lt;br&gt;
<span class="gs" title="Generic.Strong">**Keyword(s):**</span> Tutorial, TESS, TICA, SPOC, astroquery, astrocut, cutouts, ffi, tpf, tesscut &lt;br&gt;
<span class="gs" title="Generic.Strong">**Last Updated:**</span> Mar 2023 &lt;br&gt;
***
[<span class="nt" title="Name.Tag">Top of Page</span>](<span class="na" title="Name.Attribute">#top</span>)
&lt;img style="float: right;" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" alt="Space Telescope Logo" width="200px"/&gt;
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-56-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-56" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-56-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{'slideshow': {'slide_type': 'slide'}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-56-loc" role="none">
9
</td>
<td class="nb-outputs" role="none">
<h2>
<a href="#about-this-notebook">
About this Notebook
</a>
</h2>
<p>
If you have comments or questions on this notebook, please contact us through the Archive Help Desk e-mail at
<code>
archive@stsci.edu
</code>
.
</p>
<p>
<strong>
Author(s):
</strong>
Jenny V. Medina
<br>
<strong>
Keyword(s):
</strong>
Tutorial, TESS, TICA, SPOC, astroquery, astrocut, cutouts, ffi, tpf, tesscut
<br>
<strong>
Last Updated:
</strong>
Mar 2023
<br>
</p>
<hr>
<p>
<a href="#top">
Top of Page
</a>
<img alt="Space Telescope Logo" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" style="float: right;" width="200px">
</p>
</td>
</tr>
</tbody>
</table>
<table class="nb-cells-footer" hidden>
<tbody>
<tr class="total">
<th scope="row">
all cells
</th>
<th scope="row">
count
</th>
<td class="nb-ordered">
unexecuted
</td>
<td class="nb-cell_type">
56
</td>
<td class="nb-source">
</td>
<td class="nb-outputs">
23
</td>
<td class="nb-toolbar">
</td>
<td class="nb-metadata">
</td>
<td class="nb-loc">
265
</td>
</tr>
<tr class="code">
<th scope="row">
code cells
</th>
<th scope="row">
count
</th>
<td class="nb-ordered">
unexecuted
</td>
<td class="nb-cell_type">
56
</td>
<td class="nb-source">
</td>
<td class="nb-outputs">
23
</td>
<td class="nb-toolbar">
</td>
<td class="nb-metadata">
</td>
<td class="nb-loc">
265
</td>
</tr>
<tr class="markdown">
<th scope="row">
markdown cells
</th>
<th scope="row">
count
</th>
<td class="nb-ordered">
</td>
<td class="nb-cell_type">
56
</td>
<td class="nb-source">
</td>
<td class="nb-outputs">
</td>
<td class="nb-toolbar">
</td>
<td class="nb-metadata">
</td>
<td class="nb-loc">
265
</td>
</tr>
</tbody>
</table>
<dialog id="nb-metadata">
<form method="dialog">
<button formmethod="dialog">
Close
</button>
{"kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"name": "python", "version": "3.12.1", "mimetype": "text/x-python", "codemirror_mode": {"name": "ipython", "version": 3}, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py"}}
</form>
</dialog>
<form aria-labelledby="nb-notebook-label" id="notebook" name="notebook">
<label>
<input id="nb-edit-mode" name="edit" type="checkbox">
edit mode
</label>
<button aria-controls="nb-metadata" onclick="openDialog()">
metadata
</button>
<button type="submit">
Run
</button>
</form>
</main>
<section id="nb-dialogs">
<details>
<summary onclick="openDialogs()">
settings, help, &amp; diagnostics
</summary>
</details>
<dialog id="nb-help">
<h1>
<a href="#notebook-help">
notebook help
</a>
</h1>
<form>
<button formmethod="dialog">
close
</button>
<h2 id="nb-defs-label">
<a href="#nb-defs-label">
definitions
</a>
</h2>
<dl aria-labelledby="nb-defs-label">
<dt id="nb-notebook-label">
notebook
</dt>
<dd>
a collections of
<a href="#nb-cells-label">
cells
</a>
</dd>
<dd>
<dl aria-labelledby="nb-notebook-label nb-defs-label">
<dt id="nb-root-metadata-label">
metadata
</dt>
<dd id="nb-root-metadata-desc">
Notebook root-level metadata.
</dd>
<dd>
<dl>
<dt id="nb-kernelspec-label">
kernelspec
</dt>
<dd>
Kernel information.
</dd>
<dd>
<dl aria-labelledby="nb-kernelspec-label">
<dt>
kernel name
</dt>
<dd>
Name of the kernel specification.
</dd>
<dt>
display name
</dt>
<dd>
Name to display in UI.
</dd>
</dl>
</dd>
<dt id="nb-language_info-label">
language info
</dt>
<dd>
Kernel information.
</dd>
<dd>
<dl aria-labelledby="nb-language_info-label">
<dt>
programming language
</dt>
<dd>
The programming language which this kernel runs.
</dd>
<dt>
<a>
codemirror mode
</a>
</dt>
<dd>
The codemirror mode to use for code in this language.
</dd>
<dt>
file extension
</dt>
<dd>
The file extension for files in this language.
</dd>
<dt>
mimetype
</dt>
<dd>
The mimetype corresponding to files in this language.
</dd>
<dt>
pygments lexer
</dt>
<dd>
The pygments lexer to use for code in this language.
</dd>
</dl>
</dd>
<dt>
title
</dt>
<dd>
The title of the notebook document
</dd>
<dt>
authors
</dt>
<dd>
The author(s) of the notebook document
</dd>
</dl>
</dd>
<dt id="nb-cells-label">
cells
</dt>
<dd id="nb-cells-desc">
Array of cells of the current notebook.
</dd>
</dl>
</dd>
<dt id="nb-cell-label">
cell
</dt>
<dd>
<dl aria-labelledby="nb-cell-label nb-defs-label">
<dt id="nb-index-label">
index
</dt>
<dd id="nb-index-desc">
the ordinal location of the cell in the notebook, counting begins from 1.
</dd>
<dt id="nb-source-label">
source
</dt>
<dd id="nb-source-desc">
Contents of the cell, represented as an array of lines.
</dd>
<dt id="nb-metadata-label">
metadata
</dt>
<dd id="nb-metadata-desc">
Cell-level metadata.
</dd>
<dd>
<dl aria-labelledby="nb-metadata-label">
<dt>
name
</dt>
<dd>
</dd>
<dt>
tags
</dt>
<dd>
</dd>
<dt id="nb-jupyter-label">
jupyter
</dt>
<dd id="nb-jupyter-desc">
Official Jupyter Metadata for Raw Cells
</dd>
<dd>
<dl>
<dt>
source hidden
</dt>
<dd>
Whether the source is hidden.
</dd>
<dt>
outputs hidden
</dt>
<dd>
Whether the outputs are hidden.
</dd>
<dt id="nb-execution-label">
execution
</dt>
<dd id="nb-execution-desc">
Execution time for the code in the cell. This tracks time at which messages are received from iopub or shell channels
</dd>
<dd>
<dl aria-labelledby="nb-execution-label">
<dt>
execute input
</dt>
<dd>
{'description': "header.date (in ISO 8601 format) of iopub channel's execute_input message. It indicates the time at which the kernel broadcasts an execute_input message to connected frontends", 'type': 'string'}
</dd>
<dt>
execute reply
</dt>
<dd>
</dd>
</dl>
</dd>
<dt>
collapsed
</dt>
<dd>
Whether the cell's output is collapsed/expanded.
</dd>
<dt>
scrolled
</dt>
<dd>
Whether the cell's output is scrolled, unscrolled, or autoscrolled.
</dd>
</dl>
</dd>
</dl>
</dd>
<dt id="nb-cell_type-label">
cell type
</dt>
<dd>
the are three kinds of cells
<dl aria-labelledby="nb-cell_type-label">
<dt id="nb-code-label">
code
</dt>
<dd id="nb-code-desc">
Notebook code cell.
</dd>
<dt id="nb-markdown-label">
markdown
</dt>
<dd id="nb-markdown-desc">
Notebook markdown cell.
</dd>
<dt id="nb-raw-label">
raw
</dt>
<dd id="nb-raw-desc">
Notebook raw nbconvert cell.
</dd>
</dl>
</dd>
<dd id="nb-cell_type-desc">
String identifying the type of cell.
</dd>
<dt id="nb-execution_count-label">
execution count
</dt>
<dd id="nb-execution_count-desc">
The code cell's prompt number. Will be null if the cell has not been run.
</dd>
<dt id="nb-outputs-label">
outputs
</dt>
<dd id="nb-outputs-desc">
Execution, display, or stream outputs.
</dd>
<dt id="nb-attachments-label">
attachments
</dt>
<dd id="nb-attachments-desc">
Media attachments (e.g. inline images), stored as mimebundle keyed by filename.
</dd>
<dt id="nb-loc-label">
lines of code
</dt>
<dd>
lines of code in the cell, including whitespace.
</dd>
</dl>
</dd>
<dt id="nb-toolbar-label">
toolbar
</dt>
<dd>
composite widgets that allow for arrow key navigation
</dd>
</dl>
</form>
</dialog>
<button accesskey="?" aria-controls="nb-help" onclick="openDialog()">
help
</button>
</section>
<footer>
<p>
By STScI
</p>
<p>
&copy; Copyright 2022-2024
</p>
<details class="log">
<summary>
<span>
activity log
</span>
</summary>
</details>
<table aria-live="polite">
<tbody>
<tr>
<th hidden>
time
</th>
<td>
message
</td>
</tr>
</tbody>
</table>
<a accesskey="0" href="#TOP">
skip to top
</a>
</footer>
</body>
</html>
" width="100%">
</iframe>
<iframe height="600" srcdoc="<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta content="width=device-width, initial-scale=1.0" name="viewport">
<title>
Notebook
</title>
<meta content="light" name="color-scheme">
<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js">
</script>
<style type="text/css">
/* # accessible css configurations
we define these high level variables to allow for configuration during use and testing.
we are not sure what the preferred default values are for these any accessible notebooks features.
the variables specify critical degrees of freedom in the notebook style.
## notebook interface specific css variables
*/
:root {
--nb-focus-width: 3px;
--nb-accent-color: auto;
--nb-background-color-dark: #2b2a33;
--nb-background-color-light: #FFFFFF;
--nb-margin: 5%;
--nb-font-size: 16px;
--nb-font-family: serif;
--nb-line-height: 1.5;
}
/* map css variables to css properties on the `body` elements.
this mapping turns out css variables into user interfaces. */
body {
font-size: var(--nb-font-size);
font-family: var(--nb-font-family);
accent-color: var(--nb-accent-color);
margin-left: var(--nb-margin);
margin-right: var(--nb-margin);
line-height: var(--nb-line-height);
width: calc(100% - 2*var(--nb-margin));
}
/* ## notebook settings
the notebook settings interface connects configurable css features to the user.
we use dialog elements to represents an element that should be hidden by default.
it turns out there are two ways to layout `dialog` elements.
they can be used as modals or block elements.
we extract this dual purpose to provide both configurations to the user.
*/
dialog form>* {
display: block;
}
#nb-dialogs details[open]~dialog {
position: relative;
}
/* ## wcag accessibility guidelines as a feature
flexibility is the future of assistive interfaces.
it is not in the purvey of privileged developers and inaccessible systems to decide what priority accessibility is.
it is the fucking priority.
in this implementation, we want to satisfy broad user needs by making a lot of features configurable.
one configurable feature is the accessibility priority.
experienced developers may need less assistive features as they spend more time with an interface.
### buttons sizes
WCAG 2.1 defines a huge stinking AAA hit size for buttons. its beautiful.
/* satisfy AAA 2.5.5 */
.wcag-aaa button,
.wcag-aaa input[type=checkbox] {
min-height: 44px;
min-width: 44px;
}
.wcag-aaa a {
font-size: max(44px, var(--nb-font-size));
}
/* WCAG 2.2 clarified a AA button size that is used to style checkboxes and buttons. */
/* satisfy AA 2.5.8 minimum target requirement */
.wcag-aa button,
.wcag-aa input[type=checkbox] {
min-height: 24px;
min-width: 24px;
}
.wcag-aa a {
font-size: max(24px, var(--nb-font-size));
}
/* ### native textareas for AAA priority.
our AAA priority choices often ensure that third party libraries can not compromise the accessibility of notebook.
the design decisions are focused native elements so that the assistive technology users have consistent expectations and outcomes.
*/
.markdown textarea[name=source]+[role=group],
.wcag-a textarea[name=source],
.wcag-aa textarea[name=source],
.wcag-aaa .markdown textarea[name=source],
/* this group selector is very ambiguous */
.wcag-aaa textarea[name=source]+[role=group] {
display: none;
}
/* this is only needed for the lsit template variant */
ol#cells {
margin: unset;
padding: unset;
}
li.cell {
list-style-type: none;
}
/* ## notebook components layout */
td[role="none"]:not([hidden]) {
display: unset;
}
table {
border-spacing: unset;
}
main>.cell,
#cells .cell,
#cells > tbody {
display: flex;
flex-direction: column;
}
ol[reversed]#cells,
[reversed]#cells tbody {
flex-direction: column-reverse;
}
/* hide the visual output area when there are no outputs.
assistive technologies will recieve an audible notification that there are not outputs. */
fieldset[data-outputs="0"] {
display: none;
}
/* ## modifications to html defaults
### focus
there nothing to be said about this topic that [sara soueidan](https://www.sarasoueidan.com/blog/focus-indicators/) hasn't said.
we start with her [universal focus recommendation](https://www.sarasoueidan.com/blog/focus-indicators/#a-%E2%80%98universal%E2%80%99-focus-indicator).
*/
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:focus-visible {
outline: max(var(--nb-focus-width), 1px) solid;
box-shadow: 0 0 0 calc(2 * max(var(--nb-focus-width), 1px));
}
/* on firefox, the input and output become interactive when there is overflow. chrome fixed this recently find reference.*/
textarea[name=source],
.cell>td>details {
overflow: auto;
min-width: 0;
}
#nb-settings li::marker,
summary[inert]::marker {
content: "";
}
/* ### inheriting styles */
input,
select,
button {
font-family: inherit;
font-size: inherit;
}
textarea {
font-family: monospace;
font-size: inherit;
line-height: inherit;
overflow: auto;
color: unset;
}
textarea[name=source] {
box-sizing: border-box;
width: 100%;
resize: vertical;
}
/* align checkboxes with buttons */
input[type="checkbox"] {
vertical-align: middle;
}
/* ## custom class selectors */
#nb-dialogs details:not([open])~dialog:not([open]):not(:focus-within):not(:active),
/* legend:not(:focus-within):not(:active), */
details.log:not([open])+table,
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clip: rect(0 0 0 0);
clip-path: inset(50%);
height: 1px;
overflow: hidden;
position: absolute;
white-space: nowrap;
width: 1px;
}
</style>
<style id="nb-light-theme" media="screen" type="text/css">
/** accessible pygments github-light-high-contrast generated by __main__**/
pre { line-height: 125%; }
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td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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.gu { color: #023b95 } /* Generic.Subheading */
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.py { color: #023b95 } /* Name.Property */
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.l { color: #ffb757 } /* Literal */
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<!-- Load mathjax -->
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</head>
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<header aria-label="site header">
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skip to main content
</a>
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<main aria-labelledby="nb-notebook-label">
<section aria-labelledby="nb-toc">
<details open>
<summary id="toc">
<span id="nb-toc">
table of contents
</span>
</summary>
<a id="nb-toc-start">
</a>
<ol aria-labelledby="nb-toc">
</ol>
<ol>
<li>
<a href="#beginner-searching-mast-using-astroquery-mast">
Beginner: Searching MAST using astroquery.mast
</a>
<ol>
<li>
<a href="#introduction-and-goals">
Introduction and Goals:
</a>
</li>
<li>
<a href="#table-of-contents">
Table of Contents
</a>
</li>
<li>
<a href="#imports">
Imports
</a>
</li>
<li>
<a href="#three-ways-to-search-for-mast-observations">
Three Ways to Search for MAST Observations
</a>
<ol>
<li>
<a href="#1-by-region">
1. By Region
</a>
</li>
<li>
<a href="#2-by-object-name">
2. By Object Name
</a>
</li>
</ol>
</li>
<li>
<a href="#getting-associated-data-products">
Getting Associated Data Products
</a>
<ol>
<li>
<a href="#performing-a-product-query">
Performing a Product Query
</a>
</li>
<li>
<a href="#filtering-the-data-products">
Filtering the Data Products
</a>
</li>
</ol>
</li>
<li>
<a href="#downloading-products">
Downloading Products
</a>
</li>
<li>
<a href="#displaying-data">
Displaying Data
</a>
</li>
<li>
<a href="#further-reading">
Further Reading
</a>
</li>
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<a href="#about-this-notebook">
About this Notebook
</a>
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</ol>
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</details>
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<table id="cells" role="presentation">
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<textarea aria-labelledby="cell-1-source-label nb-source-label" class="nb-source" form="cell-1" id="cell-1-source" name="source" readonly rows="9">&lt;a id="top"&gt;&lt;/a&gt;
# Beginner: Searching MAST using astroquery.mast
## Introduction and Goals:
This is a beginner tutorial on accessing the [MAST Archive](https://archive.stsci.edu) using the [Astroquery API](https://astroquery.readthedocs.io/en/latest/mast/mast.html). We'll cover the major search features you might find useful when querying for observations. By the end of this tutorial, you will:
* Understand how to search for observations hosted on the MAST Archive
* Download data products corresponding to your observations of interest
* Create a visual display of the downloaded data</textarea>
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<pre><span></span><code>&lt;a id="top"&gt;&lt;/a&gt;
<span class="gh" title="Generic.Heading"># Beginner: Searching MAST using astroquery.mast</span>
<span class="gu" title="Generic.Subheading">## Introduction and Goals:</span>
This is a beginner tutorial on accessing the [<span class="nt" title="Name.Tag">MAST Archive</span>](<span class="na" title="Name.Attribute">https://archive.stsci.edu</span>) using the [<span class="nt" title="Name.Tag">Astroquery API</span>](<span class="na" title="Name.Attribute">https://astroquery.readthedocs.io/en/latest/mast/mast.html</span>). We'll cover the major search features you might find useful when querying for observations. By the end of this tutorial, you will:
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>Understand how to search for observations hosted on the MAST Archive
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>Download data products corresponding to your observations of interest
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>Create a visual display of the downloaded data
</code></pre>
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9
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<a id="top">
</a>
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<h1>
<a href="#beginner-searching-mast-using-astroquery-mast">
Beginner: Searching MAST using astroquery.mast
</a>
</h1>
<h2>
<a href="#introduction-and-goals">
Introduction and Goals:
</a>
</h2>
<p>
This is a beginner tutorial on accessing the
<a href="https://archive.stsci.edu">
MAST Archive
</a>
using the
<a href="https://astroquery.readthedocs.io/en/latest/mast/mast.html">
Astroquery API
</a>
. We'll cover the major search features you might find useful when querying for observations. By the end of this tutorial, you will:
</p>
<ul>
<li>
Understand how to search for observations hosted on the MAST Archive
</li>
<li>
Download data products corresponding to your observations of interest
</li>
<li>
Create a visual display of the downloaded data
</li>
</ul>
</td>
</tr>
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<textarea aria-labelledby="cell-2-source-label nb-source-label" class="nb-source" form="cell-2" id="cell-2-source" name="source" readonly rows="19">## Table of Contents
* [Imports](#Imports)
* [Three ways to search for MAST observations](#Three-Ways-to-Search-for-MAST-Observations)
- [By Region](#1.-By-Region)
- [By Object Name](#2.-By-Object-Name)
- [By Criteria](#crit)
* [Getting Associated Data Products](#Getting-Associated-Data-Products)
- [Performing a Product Query](#Performing-a-Product-Query)
- [Filtering Data Products](#Filtering-the-Data-Products)
* [Downloading Products](#Downloading-Products)
* [Displaying Data](#Displaying-Data)
* [Further Reading](#Further-Reading)</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Table of Contents</span>
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Imports</span>](<span class="na" title="Name.Attribute">#Imports</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Three ways to search for MAST observations</span>](<span class="na" title="Name.Attribute">#Three-Ways-to-Search-for-MAST-Observations</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">By Region</span>](<span class="na" title="Name.Attribute">#1.-By-Region</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">By Object Name</span>](<span class="na" title="Name.Attribute">#2.-By-Object-Name</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">By Criteria</span>](<span class="na" title="Name.Attribute">#crit</span>)
<span class="w" title="Text.Whitespace"> </span>
<span class="w" title="Text.Whitespace"> </span>
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Getting Associated Data Products</span>](<span class="na" title="Name.Attribute">#Getting-Associated-Data-Products</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Performing a Product Query</span>](<span class="na" title="Name.Attribute">#Performing-a-Product-Query</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Filtering Data Products</span>](<span class="na" title="Name.Attribute">#Filtering-the-Data-Products</span>)
<span class="w" title="Text.Whitespace"> </span>
<span class="w" title="Text.Whitespace"> </span>
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Downloading Products</span>](<span class="na" title="Name.Attribute">#Downloading-Products</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Displaying Data</span>](<span class="na" title="Name.Attribute">#Displaying-Data</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Further Reading</span>](<span class="na" title="Name.Attribute">#Further-Reading</span>)
</code></pre>
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<h2>
<a href="#table-of-contents">
Table of Contents
</a>
</h2>
<ul>
<li>
<p>
<a href="#Imports">
Imports
</a>
</p>
</li>
<li>
<p>
<a href="#Three-Ways-to-Search-for-MAST-Observations">
Three ways to search for MAST observations
</a>
</p>
<ul>
<li>
<a href="#1.-By-Region">
By Region
</a>
</li>
<li>
<a href="#2.-By-Object-Name">
By Object Name
</a>
</li>
<li>
<a href="#crit">
By Criteria
</a>
</li>
</ul>
</li>
<li>
<p>
<a href="#Getting-Associated-Data-Products">
Getting Associated Data Products
</a>
</p>
<ul>
<li>
<a href="#Performing-a-Product-Query">
Performing a Product Query
</a>
</li>
<li>
<a href="#Filtering-the-Data-Products">
Filtering Data Products
</a>
</li>
</ul>
</li>
<li>
<p>
<a href="#Downloading-Products">
Downloading Products
</a>
</p>
</li>
<li>
<p>
<a href="#Displaying-Data">
Displaying Data
</a>
</p>
</li>
<li>
<p>
<a href="#Further-Reading">
Further Reading
</a>
</p>
</li>
</ul>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Imports</span>
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Imports
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<textarea aria-labelledby="cell-4-source-label nb-source-label" class="nb-source" form="cell-4" id="cell-4-source" name="source" readonly rows="5">Let's start by importing the packages we need for this notebook.
* `astroquery.mast` to access the MAST API
* `astropy` to create [coordinate objects](https://docs.astropy.org/en/stable/coordinates/index.html) and access the data
* `matplotlib` to plot the data</textarea>
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<pre><span></span><code>Let's start by importing the packages we need for this notebook.
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`astroquery.mast`</span> to access the MAST API
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`astropy`</span> to create [<span class="nt" title="Name.Tag">coordinate objects</span>](<span class="na" title="Name.Attribute">https://docs.astropy.org/en/stable/coordinates/index.html</span>) and access the data
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`matplotlib`</span> to plot the data
</code></pre>
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<p>
Let's start by importing the packages we need for this notebook.
</p>
<ul>
<li>
<code>
astroquery.mast
</code>
to access the MAST API
</li>
<li>
<code>
astropy
</code>
to create
<a href="https://docs.astropy.org/en/stable/coordinates/index.html">
coordinate objects
</a>
and access the data
</li>
<li>
<code>
matplotlib
</code>
to plot the data
</li>
</ul>
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<time datetime="2024-02-02T04:06:29.986+00:00">
2024-02-02T04:06:29.986199+00:00
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<textarea aria-labelledby="cell-5-source-label nb-source-label" class="nb-source" form="cell-5" id="cell-5-source" name="source" readonly rows="8">from astropy.coordinates import SkyCoord
from astroquery.mast import Observations
from astropy.io import fits
from matplotlib.colors import SymLogNorm
import matplotlib.pyplot as plt
%matplotlib inline</textarea>
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<pre><span></span><code><span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astropy.coordinates</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">SkyCoord</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astroquery.mast</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">Observations</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astropy.io</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">fits</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">matplotlib.colors</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">SymLogNorm</span>
<span class="kn" title="Keyword.Namespace">import</span> <span class="nn" title="Name.Namespace">matplotlib.pyplot</span> <span class="k" title="Keyword">as</span> <span class="nn" title="Name.Namespace">plt</span>
<span class="o" title="Operator">%</span><span class="n" title="Name">matplotlib</span> <span class="n" title="Name">inline</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:06:29.986030Z', 'iopub.execute_input': '2024-02-02T04:06:29.986199Z', 'iopub.status.idle': '2024-02-02T04:06:30.741025Z', 'shell.execute_reply': '2024-02-02T04:06:30.740700Z'}}
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<textarea aria-labelledby="cell-6-source-label nb-source-label" class="nb-source" form="cell-6" id="cell-6-source" name="source" readonly rows="1">## Three Ways to Search for MAST Observations</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Three Ways to Search for MAST Observations</span>
</code></pre>
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<h2>
<a href="#three-ways-to-search-for-mast-observations">
Three Ways to Search for MAST Observations
</a>
</h2>
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<textarea aria-labelledby="cell-7-source-label nb-source-label" class="nb-source" form="cell-7" id="cell-7-source" name="source" readonly rows="6">### 1. By Region
To search by coordinates (and a radius), you can use `query_region`.
The coordinates can be given as a string or `astropy.coordinates` object, and the radius as a string or float object. If no radius is specified, the default is 0.2 degrees.
Let's try an example search with the coordinates (322.49324, 12.16683) and no radius.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### 1. By Region</span>
To search by coordinates (and a radius), you can use <span class="sb" title="Literal.String.Backtick">`query_region`</span>.
The coordinates can be given as a string or <span class="sb" title="Literal.String.Backtick">`astropy.coordinates`</span> object, and the radius as a string or float object. If no radius is specified, the default is 0.2 degrees.
Let's try an example search with the coordinates (322.49324, 12.16683) and no radius.
</code></pre>
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<h3>
<a href="#1-by-region">
1. By Region
</a>
</h3>
<p>
To search by coordinates (and a radius), you can use
<code>
query_region
</code>
.
</p>
<p>
The coordinates can be given as a string or
<code>
astropy.coordinates
</code>
object, and the radius as a string or float object. If no radius is specified, the default is 0.2 degrees.
</p>
<p>
Let's try an example search with the coordinates (322.49324, 12.16683) and no radius.
</p>
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2024-02-02T04:06:30.749880+00:00
</time>
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<td class="nb-end" hidden id="cell-8-end" role="none">
<time datetime="2024-02-02T04:06:35.831+00:00">
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<textarea aria-labelledby="cell-8-source-label nb-source-label" class="nb-source" form="cell-8" id="cell-8-source" name="source" readonly rows="3"># This will give a warning that the coordinates are being interpreted as an ICRS coordinate provided in degrees
obsByRegion = Observations.query_region("322.49324 12.16683")
len(obsByRegion)</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># This will give a warning that the coordinates are being interpreted as an ICRS coordinate provided in degrees</span>
<span class="n" title="Name">obsByRegion</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">query_region</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"322.49324 12.16683"</span><span class="p" title="Punctuation">)</span>
<span class="nb" title="Name.Builtin">len</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">obsByRegion</span><span class="p" title="Punctuation">)</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:06:30.749504Z', 'iopub.execute_input': '2024-02-02T04:06:30.749880Z', 'shell.execute_reply': '2024-02-02T04:06:35.831772Z', 'iopub.status.idle': '2024-02-02T04:06:35.832253Z'}}
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3
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<code>WARNING: InputWarning: Coordinate string is being interpreted as an ICRS coordinate provided in degrees. [astroquery.utils.commons]
</code>
</pre>
<pre class="plain">3043</pre>
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<textarea aria-labelledby="cell-9-source-label nb-source-label" class="nb-source" form="cell-9" id="cell-9-source" name="source" readonly rows="1">Excellent! At the time of writing, this search returns 2240 results. As the MAST archive grows, this number increases; you might see a larger value. Let's take a look a subset of the results.</textarea>
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<pre><span></span><code>Excellent! At the time of writing, this search returns 2240 results. As the MAST archive grows, this number increases; you might see a larger value. Let's take a look a subset of the results.
</code></pre>
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<p>
Excellent! At the time of writing, this search returns 2240 results. As the MAST archive grows, this number increases; you might see a larger value. Let's take a look a subset of the results.
</p>
</td>
</tr>
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2024-02-02T04:06:35.833814+00:00
</time>
</td>
<td class="nb-end" hidden id="cell-10-end" role="none">
<time datetime="2024-02-02T04:06:35.843+00:00">
2024-02-02T04:06:35.843120+00:00
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<textarea aria-labelledby="cell-10-source-label nb-source-label" class="nb-source" form="cell-10" id="cell-10-source" name="source" readonly rows="2"># Preview the first 3 results
obsByRegion[:3].show_in_notebook()</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Preview the first 3 results</span>
<span class="n" title="Name">obsByRegion</span><span class="p" title="Punctuation">[:</span><span class="mi" title="Literal.Number.Integer">3</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">show_in_notebook</span><span class="p" title="Punctuation">()</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:06:35.833650Z', 'iopub.execute_input': '2024-02-02T04:06:35.833814Z', 'iopub.status.idle': '2024-02-02T04:06:35.843409Z', 'shell.execute_reply': '2024-02-02T04:06:35.843120Z'}}
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has 1 output
</span>
<i>
Table length=3
</i>
<table class="table-striped table-bordered table-condensed" id="table140530659788416-828395">
<thead>
<tr>
<th>
idx
</th>
<th>
intentType
</th>
<th>
obs_collection
</th>
<th>
provenance_name
</th>
<th>
instrument_name
</th>
<th>
project
</th>
<th>
filters
</th>
<th>
wavelength_region
</th>
<th>
target_name
</th>
<th>
target_classification
</th>
<th>
obs_id
</th>
<th>
s_ra
</th>
<th>
s_dec
</th>
<th>
dataproduct_type
</th>
<th>
proposal_pi
</th>
<th>
calib_level
</th>
<th>
t_min
</th>
<th>
t_max
</th>
<th>
t_exptime
</th>
<th>
em_min
</th>
<th>
em_max
</th>
<th>
obs_title
</th>
<th>
t_obs_release
</th>
<th>
proposal_id
</th>
<th>
proposal_type
</th>
<th>
sequence_number
</th>
<th>
s_region
</th>
<th>
jpegURL
</th>
<th>
dataURL
</th>
<th>
dataRights
</th>
<th>
mtFlag
</th>
<th>
srcDen
</th>
<th>
obsid
</th>
<th>
distance
</th>
</tr>
</thead>
<tbody>
<tr>
<td>
0
</td>
<td>
science
</td>
<td>
TESS
</td>
<td>
SPOC
</td>
<td>
Photometer
</td>
<td>
TESS
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
TESS FFI
</td>
<td>
--
</td>
<td>
tess-s0055-1-1
</td>
<td>
325.9351193555199
</td>
<td>
11.77496602082765
</td>
<td>
image
</td>
<td>
Ricker, George
</td>
<td>
3
</td>
<td>
59796.6013437963
</td>
<td>
59823.76825269676
</td>
<td>
475.199786
</td>
<td>
600.0
</td>
<td>
1000.0
</td>
<td>
--
</td>
<td>
59841.0
</td>
<td>
N/A
</td>
<td>
--
</td>
<td>
55
</td>
<td>
POLYGON 329.837843 19.006512 333.753586 8.161861 322.106193 4.225655 318.039501 15.437198 329.837843 19.006512
</td>
<td>
--
</td>
<td>
--
</td>
<td>
PUBLIC
</td>
<td>
False
</td>
<td>
nan
</td>
<td>
95133321
</td>
<td>
0.0
</td>
</tr>
<tr>
<td>
1
</td>
<td>
science
</td>
<td>
TESS
</td>
<td>
SPOC
</td>
<td>
Photometer
</td>
<td>
TESS
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
96703881
</td>
<td>
--
</td>
<td>
tess2022217014003-s0055-0000000096703881-0242-s
</td>
<td>
322.493171
</td>
<td>
12.166862
</td>
<td>
timeseries
</td>
<td>
Ricker, George
</td>
<td>
3
</td>
<td>
59796.60416888889
</td>
<td>
59823.76815293982
</td>
<td>
120.0
</td>
<td>
600.0
</td>
<td>
1000.0
</td>
<td>
--
</td>
<td>
59841.0
</td>
<td>
G04046
</td>
<td>
--
</td>
<td>
55
</td>
<td>
CIRCLE 322.493171 12.166862 0.00138889
</td>
<td>
--
</td>
<td>
mast:TESS/product/tess2022217014003-s0055-0000000096703881-0242-s_lc.fits
</td>
<td>
PUBLIC
</td>
<td>
False
</td>
<td>
nan
</td>
<td>
93770500
</td>
<td>
0.0
</td>
</tr>
<tr>
<td>
2
</td>
<td>
science
</td>
<td>
TESS
</td>
<td>
SPOC
</td>
<td>
Photometer
</td>
<td>
TESS
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
96704587
</td>
<td>
--
</td>
<td>
tess2022217014003-s0055-0000000096704587-0242-s
</td>
<td>
322.535183231291
</td>
<td>
12.2706892440286
</td>
<td>
timeseries
</td>
<td>
Ricker, George
</td>
<td>
3
</td>
<td>
59796.60416328704
</td>
<td>
59823.76815065972
</td>
<td>
120.0
</td>
<td>
600.0
</td>
<td>
1000.0
</td>
<td>
--
</td>
<td>
59841.0
</td>
<td>
G04103
</td>
<td>
--
</td>
<td>
55
</td>
<td>
CIRCLE 322.53518323 12.27068924 0.00138889
</td>
<td>
--
</td>
<td>
mast:TESS/product/tess2022217014003-s0055-0000000096704587-0242-s_lc.fits
</td>
<td>
PUBLIC
</td>
<td>
False
</td>
<td>
nan
</td>
<td>
93770499
</td>
<td>
397.01844231118105
</td>
</tr>
</tbody>
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<tr aria-labelledby="nb-cell-label 11 cell-11-cell_type" class="cell markdown" data-index="11" data-loc="3" role="listitem">
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11
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<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-11-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-11-select">
cell type
<select form="cell-11" name="cell_type">
<option id="cell-11-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-11-source-label" class="nb-toolbar" hidden id="cell-11" name="cell-11">
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<td class="nb-start" hidden id="cell-11-start" role="none">
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<td class="nb-end" hidden id="cell-11-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-11-source-label nb-source-label" class="nb-source" form="cell-11" id="cell-11-source" name="source" readonly rows="3">The columns of the above table (there are many!) correspond to searchable fields in the MAST database. You can find the full list of criteria [here](https://mast.stsci.edu/api/v0/_c_a_o_mfields.html) and an example of performing a search by criteria [down below](#crit).
If we want to avoid that pesky warning from the search above we can create a `coordinates` object and pass it to our search. Let's try that now, and in addition, let's add a radius of 1 arcsecond to this search.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>The columns of the above table (there are many!) correspond to searchable fields in the MAST database. You can find the full list of criteria [<span class="nt" title="Name.Tag">here</span>](<span class="na" title="Name.Attribute">https://mast.stsci.edu/api/v0/_c_a_o_mfields.html</span>) and an example of performing a search by criteria [<span class="nt" title="Name.Tag">down below</span>](<span class="na" title="Name.Attribute">#crit</span>).
If we want to avoid that pesky warning from the search above we can create a <span class="sb" title="Literal.String.Backtick">`coordinates`</span> object and pass it to our search. Let's try that now, and in addition, let's add a radius of 1 arcsecond to this search.
</code></pre>
</div>
</div>
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<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-11-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-11" name="metadata" onclick="openDialog()">
metadata
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<dialog id="cell-11-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-11-loc" role="none">
3
</td>
<td class="nb-outputs" role="none">
<p>
The columns of the above table (there are many!) correspond to searchable fields in the MAST database. You can find the full list of criteria
<a href="https://mast.stsci.edu/api/v0/_c_a_o_mfields.html">
here
</a>
and an example of performing a search by criteria
<a href="#crit">
down below
</a>
.
</p>
<p>
If we want to avoid that pesky warning from the search above we can create a
<code>
coordinates
</code>
object and pass it to our search. Let's try that now, and in addition, let's add a radius of 1 arcsecond to this search.
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 12 cell-12-cell_type" class="cell code" data-index="12" data-loc="6" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-code-label nb-cell-label cell-12-loc nb-loc-label" aria-labelledby="nb-cell-label 12" class="nb-anchor" href="#12" id="12">
12
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<label class="nb-execution_count" id="cell-12-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
4
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-12-select">
cell type
<select form="cell-12" name="cell_type">
<option value="markdown">
markdown
</option>
<option id="cell-12-cell_type" selected value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
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<form aria-labelledby="cell-12-source-label" class="nb-toolbar" hidden id="cell-12" name="cell-12">
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<td class="nb-start" hidden id="cell-12-start" role="none">
<time datetime="2024-02-02T04:06:35.844+00:00">
2024-02-02T04:06:35.844741+00:00
</time>
</td>
<td class="nb-end" hidden id="cell-12-end" role="none">
<time datetime="2024-02-02T04:06:38.042+00:00">
2024-02-02T04:06:38.042035+00:00
</time>
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-12-source-label nb-source-label" class="nb-source" form="cell-12" id="cell-12-source" name="source" readonly rows="6"># Set up our coordinates object
coord = SkyCoord(290.213503, -11.800746, unit='deg')
# Same search as above, now with a radius of 1 arcsecond
obsByRegion2 = Observations.query_region(coord, radius='1s')
len(obsByRegion2)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="c1" title="Comment.Single"># Set up our coordinates object</span>
<span class="n" title="Name">coord</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">SkyCoord</span><span class="p" title="Punctuation">(</span><span class="mf" title="Literal.Number.Float">290.213503</span><span class="p" title="Punctuation">,</span> <span class="o" title="Operator">-</span><span class="mf" title="Literal.Number.Float">11.800746</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">unit</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'deg'</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Same search as above, now with a radius of 1 arcsecond</span>
<span class="n" title="Name">obsByRegion2</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">query_region</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">coord</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">radius</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'1s'</span><span class="p" title="Punctuation">)</span>
<span class="nb" title="Name.Builtin">len</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">obsByRegion2</span><span class="p" title="Punctuation">)</span>
</code></pre>
</div>
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<pre><code>
{'execution': {'iopub.status.busy': '2024-02-02T04:06:35.844605Z', 'iopub.execute_input': '2024-02-02T04:06:35.844741Z', 'iopub.status.idle': '2024-02-02T04:06:38.042874Z', 'shell.execute_reply': '2024-02-02T04:06:38.042035Z'}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-12-loc" role="none">
6
</td>
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<fieldset class="nb-outputs" data-outputs="1">
<legend aria-describedby="nb-outputs-desc" id="cell-12-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
4
</span>
<span aria-hidden="true">
]
</span>
</legend>
<span class="visually-hidden" id="cell-12-outputs-len">
has 1 output
</span>
<pre class="plain">9</pre>
</fieldset>
</td>
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<tr aria-labelledby="nb-cell-label 13 cell-13-cell_type" class="cell markdown" data-index="13" data-loc="3" role="listitem">
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<a aria-describedby="nb-markdown-label nb-cell-label cell-13-loc nb-loc-label" aria-labelledby="nb-cell-label 13" class="nb-anchor" href="#13" id="13">
13
</a>
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<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-13-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-13-select">
cell type
<select form="cell-13" name="cell_type">
<option id="cell-13-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-13-source-label" class="nb-toolbar" hidden id="cell-13" name="cell-13">
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<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-13-source-label nb-source-label" class="nb-source" form="cell-13" id="cell-13-source" name="source" readonly rows="3">Sanity check: we expect that as the radius gets smaller, we get fewer results. In this case, only 15 results are returned.
Let's take a look at the results again, but let's limit the columns that get displayed.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>Sanity check: we expect that as the radius gets smaller, we get fewer results. In this case, only 15 results are returned.
Let's take a look at the results again, but let's limit the columns that get displayed.
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-13-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-13" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-13-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
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</td>
<td class="nb-loc" hidden id="cell-13-loc" role="none">
3
</td>
<td class="nb-outputs" role="none">
<p>
Sanity check: we expect that as the radius gets smaller, we get fewer results. In this case, only 15 results are returned.
</p>
<p>
Let's take a look at the results again, but let's limit the columns that get displayed.
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 14 cell-14-cell_type" class="cell code" data-index="14" data-loc="6" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-code-label nb-cell-label cell-14-loc nb-loc-label" aria-labelledby="nb-cell-label 14" class="nb-anchor" href="#14" id="14">
14
</a>
</td>
<td class="nb-execution_count" role="none">
<label class="nb-execution_count" id="cell-14-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
5
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-14-select">
cell type
<select form="cell-14" name="cell_type">
<option value="markdown">
markdown
</option>
<option id="cell-14-cell_type" selected value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-14-source-label" class="nb-toolbar" hidden id="cell-14" name="cell-14">
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</fieldset>
</form>
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<td class="nb-start" hidden id="cell-14-start" role="none">
<time datetime="2024-02-02T04:06:38.045+00:00">
2024-02-02T04:06:38.045735+00:00
</time>
</td>
<td class="nb-end" hidden id="cell-14-end" role="none">
<time datetime="2024-02-02T04:06:38.053+00:00">
2024-02-02T04:06:38.053047+00:00
</time>
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-14-source-label nb-source-label" class="nb-source" form="cell-14" id="cell-14-source" name="source" readonly rows="6"># Let's limit the number of columns we see at once
columns = ['obs_collection', 'intentType', 'instrument_name',
'target_name', 't_exptime', 'filters', 'dataproduct_type']
# Show the results with the above columns only
obsByRegion2[columns].show_in_notebook()</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="c1" title="Comment.Single"># Let's limit the number of columns we see at once</span>
<span class="n" title="Name">columns</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'obs_collection'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'intentType'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'instrument_name'</span><span class="p" title="Punctuation">,</span>
<span class="s1" title="Literal.String.Single">'target_name'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'t_exptime'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'filters'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'dataproduct_type'</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># Show the results with the above columns only</span>
<span class="n" title="Name">obsByRegion2</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">columns</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">show_in_notebook</span><span class="p" title="Punctuation">()</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-14-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-14" name="metadata" onclick="openDialog()">
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</button>
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<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{'execution': {'iopub.status.busy': '2024-02-02T04:06:38.045379Z', 'iopub.execute_input': '2024-02-02T04:06:38.045735Z', 'shell.execute_reply': '2024-02-02T04:06:38.053047Z', 'iopub.status.idle': '2024-02-02T04:06:38.053349Z'}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-14-loc" role="none">
6
</td>
<td class="nb-outputs" role="none">
<fieldset class="nb-outputs" data-outputs="1">
<legend aria-describedby="nb-outputs-desc" id="cell-14-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
5
</span>
<span aria-hidden="true">
]
</span>
</legend>
<span class="visually-hidden" id="cell-14-outputs-len">
has 1 output
</span>
<i>
Table length=9
</i>
<table class="table-striped table-bordered table-condensed" id="table140531753466912-974959">
<thead>
<tr>
<th>
idx
</th>
<th>
obs_collection
</th>
<th>
intentType
</th>
<th>
instrument_name
</th>
<th>
target_name
</th>
<th>
t_exptime
</th>
<th>
filters
</th>
<th>
dataproduct_type
</th>
</tr>
</thead>
<tbody>
<tr>
<td>
0
</td>
<td>
TESS
</td>
<td>
science
</td>
<td>
Photometer
</td>
<td>
TESS FFI
</td>
<td>
475.199781
</td>
<td>
TESS
</td>
<td>
image
</td>
</tr>
<tr>
<td>
1
</td>
<td>
PS1
</td>
<td>
science
</td>
<td>
GPC1
</td>
<td>
1126.007
</td>
<td>
731.0
</td>
<td>
g
</td>
<td>
image
</td>
</tr>
<tr>
<td>
2
</td>
<td>
PS1
</td>
<td>
science
</td>
<td>
GPC1
</td>
<td>
1126.007
</td>
<td>
900.0
</td>
<td>
i
</td>
<td>
image
</td>
</tr>
<tr>
<td>
3
</td>
<td>
PS1
</td>
<td>
science
</td>
<td>
GPC1
</td>
<td>
1126.007
</td>
<td>
876.0
</td>
<td>
r
</td>
<td>
image
</td>
</tr>
<tr>
<td>
4
</td>
<td>
PS1
</td>
<td>
science
</td>
<td>
GPC1
</td>
<td>
1126.007
</td>
<td>
580.0
</td>
<td>
y
</td>
<td>
image
</td>
</tr>
<tr>
<td>
5
</td>
<td>
PS1
</td>
<td>
science
</td>
<td>
GPC1
</td>
<td>
1126.007
</td>
<td>
480.0
</td>
<td>
z
</td>
<td>
image
</td>
</tr>
<tr>
<td>
6
</td>
<td>
HLSP
</td>
<td>
science
</td>
<td>
Photometer
</td>
<td>
TICA FFI
</td>
<td>
475.2
</td>
<td>
TESS
</td>
<td>
image
</td>
</tr>
<tr>
<td>
7
</td>
<td>
GALEX
</td>
<td>
science
</td>
<td>
GALEX
</td>
<td>
AIS_246_1_35
</td>
<td>
176.0
</td>
<td>
NUV
</td>
<td>
image
</td>
</tr>
<tr>
<td>
8
</td>
<td>
GALEX
</td>
<td>
science
</td>
<td>
GALEX
</td>
<td>
AIS_246_1_35
</td>
<td>
176.0
</td>
<td>
FUV
</td>
<td>
image
</td>
</tr>
</tbody>
</table>
<style>
table.dataTable {clear: both; width: auto !important; margin: 0 !important;}
.dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{
display: inline-block; margin-right: 1em; }
.paginate_button { margin-right: 5px; }
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<textarea aria-labelledby="cell-15-source-label nb-source-label" class="nb-source" form="cell-15" id="cell-15-source" name="source" readonly rows="1">This "streamlined" view is helpful: it avoids visual clutter and helps you to focus on the fields that are most relevant to your search.</textarea>
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<pre><span></span><code>This "streamlined" view is helpful: it avoids visual clutter and helps you to focus on the fields that are most relevant to your search.
</code></pre>
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<p>
This "streamlined" view is helpful: it avoids visual clutter and helps you to focus on the fields that are most relevant to your search.
</p>
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<textarea aria-labelledby="cell-16-source-label nb-source-label" class="nb-source" form="cell-16" id="cell-16-source" name="source" readonly rows="5">### 2. By Object Name
To search for an object by name, you can use the `query.object` function. This function also optionally allows you to specify a radius.
The object name is first resolved to coordinates by a call to the [Simbad](https://simbad.unistra.fr/simbad/) and [NED](https://ned.ipac.caltech.edu/Documents/Overview) archives. Then, the search proceeeds based on these coordinates.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### 2. By Object Name</span>
To search for an object by name, you can use the <span class="sb" title="Literal.String.Backtick">`query.object`</span> function. This function also optionally allows you to specify a radius.
The object name is first resolved to coordinates by a call to the [<span class="nt" title="Name.Tag">Simbad</span>](<span class="na" title="Name.Attribute">https://simbad.unistra.fr/simbad/</span>) and [<span class="nt" title="Name.Tag">NED</span>](<span class="na" title="Name.Attribute">https://ned.ipac.caltech.edu/Documents/Overview</span>) archives. Then, the search proceeeds based on these coordinates.
</code></pre>
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5
</td>
<td class="nb-outputs" role="none">
<h3>
<a href="#2-by-object-name">
2. By Object Name
</a>
</h3>
<p>
To search for an object by name, you can use the
<code>
query.object
</code>
function. This function also optionally allows you to specify a radius.
</p>
<p>
The object name is first resolved to coordinates by a call to the
<a href="https://simbad.unistra.fr/simbad/">
Simbad
</a>
and
<a href="https://ned.ipac.caltech.edu/Documents/Overview">
NED
</a>
archives. Then, the search proceeeds based on these coordinates.
</p>
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<time datetime="2024-02-02T04:06:38.054+00:00">
2024-02-02T04:06:38.054728+00:00
</time>
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<td class="nb-end" hidden id="cell-17-end" role="none">
<time datetime="2024-02-02T04:06:57.946+00:00">
2024-02-02T04:06:57.946034+00:00
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<textarea aria-labelledby="cell-17-source-label nb-source-label" class="nb-source" form="cell-17" id="cell-17-source" name="source" readonly rows="1">obsByName = Observations.query_object("M51",radius=".005 deg")</textarea>
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<pre><span></span><code><span class="n" title="Name">obsByName</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">query_object</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"M51"</span><span class="p" title="Punctuation">,</span><span class="n" title="Name">radius</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">".005 deg"</span><span class="p" title="Punctuation">)</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:06:38.054577Z', 'iopub.execute_input': '2024-02-02T04:06:38.054728Z', 'shell.execute_reply': '2024-02-02T04:06:57.946034Z', 'iopub.status.idle': '2024-02-02T04:06:57.946462Z'}}
</code></pre>
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1
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In 6 has
0
outputs.
</span>
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<time datetime="2024-02-02T04:06:57.963+00:00">
2024-02-02T04:06:57.963996+00:00
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<textarea aria-labelledby="cell-18-source-label nb-source-label" class="nb-source" form="cell-18" id="cell-18-source" name="source" readonly rows="4">print("Number of results:",len(obsByName))
# Here we just print the values, but you can use '.show_in_table' like we did in the previous cell
print(obsByName[:10])</textarea>
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<pre><span></span><code><span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"Number of results:"</span><span class="p" title="Punctuation">,</span><span class="nb" title="Name.Builtin">len</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">obsByName</span><span class="p" title="Punctuation">))</span>
<span class="c1" title="Comment.Single"># Here we just print the values, but you can use '.show_in_table' like we did in the previous cell</span>
<span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">obsByName</span><span class="p" title="Punctuation">[:</span><span class="mi" title="Literal.Number.Integer">10</span><span class="p" title="Punctuation">])</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:06:57.948887Z', 'iopub.execute_input': '2024-02-02T04:06:57.949155Z', 'iopub.status.idle': '2024-02-02T04:06:57.964779Z', 'shell.execute_reply': '2024-02-02T04:06:57.963996Z'}}
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<fieldset class="nb-outputs" data-outputs="1">
<legend aria-describedby="nb-outputs-desc" id="cell-18-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
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</span>
<span>
7
</span>
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</span>
</legend>
<span class="visually-hidden" id="cell-18-outputs-len">
has 1 output
</span>
<pre class="stdout">
<code>Number of results: 1749
intentType obs_collection provenance_name ... srcDen obsid distance
---------- -------------- --------------- ... ------ -------- --------
science TESS SPOC ... nan 27545566 0.0
science TESS SPOC ... nan 27347170 0.0
science TESS SPOC ... nan 27386992 0.0
science TESS SPOC ... nan 83163378 0.0
science SWIFT -- ... nan 1628611 0.0
science SWIFT -- ... 5885.0 1491576 0.0
science SWIFT -- ... 5885.0 1491575 0.0
science SWIFT -- ... 5885.0 1493598 0.0
science SWIFT -- ... 5885.0 1493597 0.0
science SWIFT -- ... 5885.0 1493982 0.0
</code>
</pre>
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</span>
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<option id="cell-19-cell_type" selected value="markdown">
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</option>
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<textarea aria-labelledby="cell-19-source-label nb-source-label" class="nb-source" form="cell-19" id="cell-19-source" name="source" readonly rows="2">For some special catalogs, like those used with Kepler, K2, and TESS, astroquery performs a direct lookup using the MAST catalog. It is important to include both the **_catalog identifier_** and number when searching one of these datasets; for example, to query the TESS Input catalog you would use "TIC 261136679", rather than a plain
"261136679".</textarea>
<div aria-labelledby="nb-source-label" role="group">
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<pre><span></span><code>For some special catalogs, like those used with Kepler, K2, and TESS, astroquery performs a direct lookup using the MAST catalog. It is important to include both the <span class="gs" title="Generic.Strong">**_catalog identifier_**</span> and number when searching one of these datasets; for example, to query the TESS Input catalog you would use "TIC 261136679", rather than a plain
"261136679".
</code></pre>
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{}
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2
</td>
<td class="nb-outputs" role="none">
<p>
For some special catalogs, like those used with Kepler, K2, and TESS, astroquery performs a direct lookup using the MAST catalog. It is important to include both the
<strong>
<em>
catalog identifier
</em>
</strong>
and number when searching one of these datasets; for example, to query the TESS Input catalog you would use "TIC 261136679", rather than a plain
"261136679".
</p>
</td>
</tr>
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2024-02-02T04:06:57.967817+00:00
</time>
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<td class="nb-end" hidden id="cell-20-end" role="none">
<time datetime="2024-02-02T04:08:11.418+00:00">
2024-02-02T04:08:11.418251+00:00
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<textarea aria-labelledby="cell-20-source-label nb-source-label" class="nb-source" form="cell-20" id="cell-20-source" name="source" readonly rows="1">obsByTessName = Observations.query_object("TIC 261136679", radius="1s")</textarea>
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<div class="highlight">
<pre><span></span><code><span class="n" title="Name">obsByTessName</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">query_object</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"TIC 261136679"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">radius</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"1s"</span><span class="p" title="Punctuation">)</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:06:57.967377Z', 'iopub.execute_input': '2024-02-02T04:06:57.967817Z', 'shell.execute_reply': '2024-02-02T04:08:11.418251Z', 'iopub.status.idle': '2024-02-02T04:08:11.418828Z'}}
</code></pre>
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<textarea aria-labelledby="cell-21-source-label nb-source-label" class="nb-source" form="cell-21" id="cell-21-source" name="source" readonly rows="3"># As before, we use columns to limit the display
columns = ['obs_collection', 'wavelength_region', 'provenance_name', 't_min', 't_max', 'obsid']
obsByTessName[columns].show_in_notebook(display_length=10)</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># As before, we use columns to limit the display</span>
<span class="n" title="Name">columns</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'obs_collection'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'wavelength_region'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'provenance_name'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'t_min'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'t_max'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'obsid'</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">obsByTessName</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">columns</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">show_in_notebook</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">display_length</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">10</span><span class="p" title="Punctuation">)</span>
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<span class="visually-hidden" id="cell-21-outputs-len">
has 1 output
</span>
<i>
Table length=166
</i>
<table class="table-striped table-bordered table-condensed" id="table140530652170192-606651">
<thead>
<tr>
<th>
idx
</th>
<th>
obs_collection
</th>
<th>
wavelength_region
</th>
<th>
provenance_name
</th>
<th>
t_min
</th>
<th>
t_max
</th>
<th>
obsid
</th>
</tr>
</thead>
<tbody>
<tr>
<td>
0
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58324.81304390046
</td>
<td>
58352.666903854166
</td>
<td>
60865631
</td>
</tr>
<tr>
<td>
1
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58410.41593530092
</td>
<td>
58436.33232072917
</td>
<td>
61132377
</td>
</tr>
<tr>
<td>
2
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58516.85312458334
</td>
<td>
58541.49921487269
</td>
<td>
62468248
</td>
</tr>
<tr>
<td>
3
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58596.27096596065
</td>
<td>
58623.37541814815
</td>
<td>
65153352
</td>
</tr>
<tr>
<td>
4
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58624.45954434028
</td>
<td>
58652.37651001157
</td>
<td>
65180725
</td>
</tr>
<tr>
<td>
5
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58653.41816987268
</td>
<td>
58681.85536055556
</td>
<td>
65462606
</td>
</tr>
<tr>
<td>
6
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59035.7791077
</td>
<td>
59060.13994723
</td>
<td>
27797249
</td>
</tr>
<tr>
<td>
7
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59061.34744424
</td>
<td>
59086.59716169
</td>
<td>
27844486
</td>
</tr>
<tr>
<td>
8
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59144.0128561
</td>
<td>
59169.44312553
</td>
<td>
28131771
</td>
</tr>
<tr>
<td>
9
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59228.2486492
</td>
<td>
59253.56140963
</td>
<td>
28364165
</td>
</tr>
<tr>
<td>
10
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59254.48474479167
</td>
<td>
59279.47805423611
</td>
<td>
60737942
</td>
</tr>
<tr>
<td>
11
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59333.35398652778
</td>
<td>
59360.04870423611
</td>
<td>
61660222
</td>
</tr>
<tr>
<td>
12
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59361.27173300926
</td>
<td>
59389.21646454861
</td>
<td>
62242849
</td>
</tr>
<tr>
<td>
13
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59962.292137743054
</td>
<td>
59987.72299065972
</td>
<td>
118311751
</td>
</tr>
<tr>
<td>
14
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59987.93332278935
</td>
<td>
60013.651212337965
</td>
<td>
126301646
</td>
</tr>
<tr>
<td>
15
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60040.60811458333
</td>
<td>
60067.529729918984
</td>
<td>
140242238
</td>
</tr>
<tr>
<td>
16
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60068.23419944444
</td>
<td>
60096.08870777778
</td>
<td>
152374421
</td>
</tr>
<tr>
<td>
17
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60097.16937944444
</td>
<td>
60125.92666236111
</td>
<td>
167876913
</td>
</tr>
<tr>
<td>
18
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60126.13727077546
</td>
<td>
60153.89626454861
</td>
<td>
172373724
</td>
</tr>
<tr>
<td>
19
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60154.10605644676
</td>
<td>
60181.6381984375
</td>
<td>
178063767
</td>
</tr>
<tr>
<td>
20
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58324.7940765625
</td>
<td>
58352.67695946759
</td>
<td>
60835895
</td>
</tr>
<tr>
<td>
21
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58324.79409923611
</td>
<td>
58436.348750717596
</td>
<td>
62470298
</td>
</tr>
<tr>
<td>
22
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58324.79409923611
</td>
<td>
58541.4991196412
</td>
<td>
63394803
</td>
</tr>
<tr>
<td>
23
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58324.794099224535
</td>
<td>
58681.85791775463
</td>
<td>
65486302
</td>
</tr>
<tr>
<td>
24
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58324.794076527774
</td>
<td>
59253.563698715276
</td>
<td>
61531517
</td>
</tr>
<tr>
<td>
25
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58324.794076527774
</td>
<td>
59389.21878765046
</td>
<td>
62342022
</td>
</tr>
<tr>
<td>
26
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58324.79407649305
</td>
<td>
60096.12668114583
</td>
<td>
160348376
</td>
</tr>
<tr>
<td>
27
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58324.79407649305
</td>
<td>
60181.633050034725
</td>
<td>
198774334
</td>
</tr>
<tr>
<td>
28
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58410.39922670139
</td>
<td>
58436.33343722222
</td>
<td>
61114378
</td>
</tr>
<tr>
<td>
29
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58516.861139479166
</td>
<td>
58541.49909662037
</td>
<td>
62459906
</td>
</tr>
<tr>
<td>
30
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58596.276727349534
</td>
<td>
58623.392479166665
</td>
<td>
65142109
</td>
</tr>
<tr>
<td>
31
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58624.454985474535
</td>
<td>
58652.392712280096
</td>
<td>
65182732
</td>
</tr>
<tr>
<td>
32
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
58653.42048966435
</td>
<td>
58681.85789472222
</td>
<td>
65462812
</td>
</tr>
<tr>
<td>
33
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59035.77864574
</td>
<td>
59060.14323613
</td>
<td>
27811411
</td>
</tr>
<tr>
<td>
34
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59035.77864574
</td>
<td>
59060.14231023
</td>
<td>
27803712
</td>
</tr>
<tr>
<td>
35
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59061.35063314
</td>
<td>
59086.59764027
</td>
<td>
27823276
</td>
</tr>
<tr>
<td>
36
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59061.35063314
</td>
<td>
59086.59740878
</td>
<td>
27820792
</td>
</tr>
<tr>
<td>
37
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59144.01273429
</td>
<td>
59169.44372635
</td>
<td>
28062277
</td>
</tr>
<tr>
<td>
38
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59144.01273429
</td>
<td>
59169.44280044
</td>
<td>
28058401
</td>
</tr>
<tr>
<td>
39
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59228.248104398146
</td>
<td>
59253.5646246875
</td>
<td>
28289741
</td>
</tr>
<tr>
<td>
40
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59228.26477123843
</td>
<td>
59253.56369873843
</td>
<td>
28306022
</td>
</tr>
<tr>
<td>
41
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59333.354320555554
</td>
<td>
59360.053113125
</td>
<td>
61592898
</td>
</tr>
<tr>
<td>
42
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59333.354320555554
</td>
<td>
59360.05195570602
</td>
<td>
61598395
</td>
</tr>
<tr>
<td>
43
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59361.27141094908
</td>
<td>
59389.21994502315
</td>
<td>
62018551
</td>
</tr>
<tr>
<td>
44
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59361.27141094908
</td>
<td>
59389.21878761574
</td>
<td>
62005566
</td>
</tr>
<tr>
<td>
45
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59962.29642171296
</td>
<td>
59987.72454979167
</td>
<td>
117955554
</td>
</tr>
<tr>
<td>
46
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59962.29642171296
</td>
<td>
59987.71066060185
</td>
<td>
117955549
</td>
</tr>
<tr>
<td>
47
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59987.938443576386
</td>
<td>
60013.65217048611
</td>
<td>
124157589
</td>
</tr>
<tr>
<td>
48
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
59987.938443576386
</td>
<td>
60013.648698171295
</td>
<td>
124157576
</td>
</tr>
<tr>
<td>
49
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60040.61321773148
</td>
<td>
60067.53207005787
</td>
<td>
139095591
</td>
</tr>
<tr>
<td>
50
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60040.61321773148
</td>
<td>
60067.53183857639
</td>
<td>
139095580
</td>
</tr>
<tr>
<td>
51
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60068.238792662036
</td>
<td>
60096.12945888889
</td>
<td>
151530181
</td>
</tr>
<tr>
<td>
52
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60068.238792662036
</td>
<td>
60096.12945888889
</td>
<td>
151530185
</td>
</tr>
<tr>
<td>
53
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60097.173916099535
</td>
<td>
60125.92817840278
</td>
<td>
173755271
</td>
</tr>
<tr>
<td>
54
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60097.173916099535
</td>
<td>
60125.92817840278
</td>
<td>
173755263
</td>
</tr>
<tr>
<td>
55
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60126.14206766204
</td>
<td>
60153.896710891204
</td>
<td>
171667548
</td>
</tr>
<tr>
<td>
56
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60126.14206766204
</td>
<td>
60153.88490546296
</td>
<td>
171667556
</td>
</tr>
<tr>
<td>
57
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60154.11129803241
</td>
<td>
60181.63929988426
</td>
<td>
176755217
</td>
</tr>
<tr>
<td>
58
</td>
<td>
TESS
</td>
<td>
Optical
</td>
<td>
SPOC
</td>
<td>
60154.11546465278
</td>
<td>
60181.62610565972
</td>
<td>
176755222
</td>
</tr>
<tr>
<td>
59
</td>
<td>
SWIFT
</td>
<td>
UV
</td>
<td>
--
</td>
<td>
57393.0239236
</td>
<td>
57393.8759838
</td>
<td>
1563441
</td>
</tr>
<tr>
<td>
60
</td>
<td>
SWIFT
</td>
<td>
UV;OPTICAL
</td>
<td>
--
</td>
<td>
57393.028831
</td>
<td>
57393.8812153
</td>
<td>
1563562
</td>
</tr>
<tr>
<td>
61
</td>
<td>
SWIFT
</td>
<td>
UV;OPTICAL
</td>
<td>
--
</td>
<td>
57393.0263426
</td>
<td>
57393.8776157
</td>
<td>
1563440
</td>
</tr>
<tr>
<td>
62
</td>
<td>
SWIFT
</td>
<td>
OPTICAL
</td>
<td>
--
</td>
<td>
57393.027963
</td>
<td>
57393.8784491
</td>
<td>
1563561
</td>
</tr>
<tr>
<td>
63
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.7453481
</td>
<td>
53109.74577287
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
64
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.74607039
</td>
<td>
53109.74700492
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
65
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.7438678
</td>
<td>
53109.74409839
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
66
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.74338169
</td>
<td>
53109.74361223
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
67
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.74338169
</td>
<td>
53109.74361223
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
68
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.74727925
</td>
<td>
53109.74821377
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
69
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.7453481
</td>
<td>
53109.74577287
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
70
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.7446491
</td>
<td>
53109.74507385
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
71
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.7438678
</td>
<td>
53109.74409839
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
72
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.7446491
</td>
<td>
53109.74507385
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
73
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.74607039
</td>
<td>
53109.74700492
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
74
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53109.74727925
</td>
<td>
53109.74821377
</td>
<td>
1737024
</td>
</tr>
<tr>
<td>
75
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53103.63710178
</td>
<td>
53103.64255711
</td>
<td>
1634700
</td>
</tr>
<tr>
<td>
76
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53103.63578501
</td>
<td>
53103.63662846
</td>
<td>
1634700
</td>
</tr>
<tr>
<td>
77
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53052.18553685
</td>
<td>
53052.19040694
</td>
<td>
1739236
</td>
</tr>
<tr>
<td>
78
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53052.18553685
</td>
<td>
53052.19040694
</td>
<td>
1739236
</td>
</tr>
<tr>
<td>
79
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53052.18553685
</td>
<td>
53052.19040694
</td>
<td>
1739236
</td>
</tr>
<tr>
<td>
80
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
53052.18553685
</td>
<td>
53052.19040694
</td>
<td>
1739236
</td>
</tr>
<tr>
<td>
81
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
54599.08378252
</td>
<td>
54599.08610149
</td>
<td>
1699737
</td>
</tr>
<tr>
<td>
82
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
54599.08378252
</td>
<td>
54599.08610149
</td>
<td>
1699737
</td>
</tr>
<tr>
<td>
83
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
54599.08378252
</td>
<td>
54599.08610149
</td>
<td>
1699737
</td>
</tr>
<tr>
<td>
84
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
54599.08378252
</td>
<td>
54599.08610149
</td>
<td>
1699737
</td>
</tr>
<tr>
<td>
85
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
nan
</td>
<td>
nan
</td>
<td>
1695305
</td>
</tr>
<tr>
<td>
86
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
nan
</td>
<td>
nan
</td>
<td>
1695305
</td>
</tr>
<tr>
<td>
87
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
nan
</td>
<td>
nan
</td>
<td>
1695305
</td>
</tr>
<tr>
<td>
88
</td>
<td>
SPITZER_SHA
</td>
<td>
Infrared
</td>
<td>
SSC Pipeline
</td>
<td>
nan
</td>
<td>
nan
</td>
<td>
1695305
</td>
</tr>
<tr>
<td>
89
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58324.81385555
</td>
<td>
58352.66771573
</td>
<td>
32119741
</td>
</tr>
<tr>
<td>
90
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58324.81385555
</td>
<td>
58352.66771573
</td>
<td>
32116730
</td>
</tr>
<tr>
<td>
91
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58324.81385555
</td>
<td>
58352.66771573
</td>
<td>
32121978
</td>
</tr>
<tr>
<td>
92
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58324.81385555
</td>
<td>
58352.66771573
</td>
<td>
32125013
</td>
</tr>
<tr>
<td>
93
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58410.41675562
</td>
<td>
58436.3331454
</td>
<td>
32156777
</td>
</tr>
<tr>
<td>
94
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58410.41675562
</td>
<td>
58436.3331454
</td>
<td>
32164982
</td>
</tr>
<tr>
<td>
95
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58516.8539358
</td>
<td>
58541.50002631
</td>
<td>
32204419
</td>
</tr>
<tr>
<td>
96
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58516.8539358
</td>
<td>
58541.50002631
</td>
<td>
32206316
</td>
</tr>
<tr>
<td>
97
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58516.8539358
</td>
<td>
58541.50002631
</td>
<td>
32212892
</td>
</tr>
<tr>
<td>
98
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58516.8539358
</td>
<td>
58541.50002631
</td>
<td>
32208188
</td>
</tr>
<tr>
<td>
99
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58596.27177792
</td>
<td>
58623.37623035
</td>
<td>
32250860
</td>
</tr>
<tr>
<td>
100
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58596.27177792
</td>
<td>
58623.37623035
</td>
<td>
32240741
</td>
</tr>
<tr>
<td>
101
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58624.46036142
</td>
<td>
58652.3773273
</td>
<td>
32256445
</td>
</tr>
<tr>
<td>
102
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58653.41898715
</td>
<td>
58681.85617807
</td>
<td>
32266203
</td>
</tr>
<tr>
<td>
103
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58653.41898715
</td>
<td>
58681.85617807
</td>
<td>
32277156
</td>
</tr>
<tr>
<td>
104
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58653.41898715
</td>
<td>
58681.85617807
</td>
<td>
32267875
</td>
</tr>
<tr>
<td>
105
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
ELEANOR
</td>
<td>
58653.41898715
</td>
<td>
58681.85617807
</td>
<td>
32272118
</td>
</tr>
<tr>
<td>
106
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
GSFC-ELEANOR-LITE
</td>
<td>
58324.81304390046
</td>
<td>
58352.666903854166
</td>
<td>
84704022
</td>
</tr>
<tr>
<td>
107
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
GSFC-ELEANOR-LITE
</td>
<td>
58410.41593530092
</td>
<td>
58436.33232072917
</td>
<td>
116010112
</td>
</tr>
<tr>
<td>
108
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
GSFC-ELEANOR-LITE
</td>
<td>
58516.85312458334
</td>
<td>
58541.49921487269
</td>
<td>
161104956
</td>
</tr>
<tr>
<td>
109
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
58324.82476052
</td>
<td>
58352.65764309
</td>
<td>
59455369
</td>
</tr>
<tr>
<td>
110
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
58410.42714117
</td>
<td>
58436.32246784
</td>
<td>
58459774
</td>
</tr>
<tr>
<td>
111
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
58516.86404518737
</td>
<td>
58541.4895025054
</td>
<td>
55003357
</td>
</tr>
<tr>
<td>
112
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
58596.28241227
</td>
<td>
58623.36621971
</td>
<td>
49252805
</td>
</tr>
<tr>
<td>
113
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
58624.47039223
</td>
<td>
58652.36645256
</td>
<td>
47144300
</td>
</tr>
<tr>
<td>
114
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
58653.42895219
</td>
<td>
58681.84552431
</td>
<td>
45092053
</td>
</tr>
<tr>
<td>
115
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
59035.78294106945
</td>
<td>
59060.13688375149
</td>
<td>
63917481
</td>
</tr>
<tr>
<td>
116
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
59061.35215103766
</td>
<td>
59086.59476037184
</td>
<td>
64550682
</td>
</tr>
<tr>
<td>
117
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
59144.01702968962
</td>
<td>
59169.44015167421
</td>
<td>
66665691
</td>
</tr>
<tr>
<td>
118
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
59228.25239934493
</td>
<td>
59253.5582715068
</td>
<td>
69866855
</td>
</tr>
<tr>
<td>
119
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
59333.35861628503
</td>
<td>
59360.04652938852
</td>
<td>
75350117
</td>
</tr>
<tr>
<td>
120
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
59361.275705982
</td>
<td>
59389.21336070169
</td>
<td>
77972093
</td>
</tr>
<tr>
<td>
121
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
59962.29377178708
</td>
<td>
59987.72236311529
</td>
<td>
163587542
</td>
</tr>
<tr>
<td>
122
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
59987.935330717824
</td>
<td>
60013.65114138601
</td>
<td>
164976661
</td>
</tr>
<tr>
<td>
123
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
60040.61010591127
</td>
<td>
60067.529653193895
</td>
<td>
173966129
</td>
</tr>
<tr>
<td>
124
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
60068.23568039574
</td>
<td>
60096.08792077331
</td>
<td>
183888484
</td>
</tr>
<tr>
<td>
125
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
60097.171267030295
</td>
<td>
60125.92599268537
</td>
<td>
186194451
</td>
</tr>
<tr>
<td>
126
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
60126.13895624876
</td>
<td>
60153.895683024544
</td>
<td>
199776093
</td>
</tr>
<tr>
<td>
127
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
QLP
</td>
<td>
60154.108649806585
</td>
<td>
60181.63827206567
</td>
<td>
200558578
</td>
</tr>
<tr>
<td>
128
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TASOC
</td>
<td>
58324.8143211136
</td>
<td>
58352.66803850389
</td>
<td>
80761526
</td>
</tr>
<tr>
<td>
129
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TASOC
</td>
<td>
58410.41670220283
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<td>
58436.3328624304
</td>
<td>
89049638
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<tr>
<td>
130
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TASOC
</td>
<td>
58324.79487706543
</td>
<td>
58352.67776024473
</td>
<td>
79395544
</td>
</tr>
<tr>
<td>
131
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TASOC
</td>
<td>
58410.40003614325
</td>
<td>
58436.34952857239
</td>
<td>
88546893
</td>
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<tr>
<td>
132
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
58324.81352074074
</td>
<td>
58352.66723747685
</td>
<td>
59804479
</td>
</tr>
<tr>
<td>
133
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
58410.41589297454
</td>
<td>
58436.33204836806
</td>
<td>
60274783
</td>
</tr>
<tr>
<td>
134
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
58516.852806030096
</td>
<td>
58541.49909662037
</td>
<td>
62618178
</td>
</tr>
<tr>
<td>
135
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
58596.29200541667
</td>
<td>
58623.375812314815
</td>
<td>
63179060
</td>
</tr>
<tr>
<td>
136
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
58624.4591521875
</td>
<td>
58652.37604560185
</td>
<td>
63279269
</td>
</tr>
<tr>
<td>
137
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
58653.438545219906
</td>
<td>
58681.85511697917
</td>
<td>
63547796
</td>
</tr>
<tr>
<td>
138
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
59035.77864574
</td>
<td>
59060.13953249
</td>
<td>
53633452
</td>
</tr>
<tr>
<td>
139
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
59061.35479975
</td>
<td>
59086.59740878
</td>
<td>
53779499
</td>
</tr>
<tr>
<td>
140
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
59144.01273429398
</td>
<td>
59169.442800439814
</td>
<td>
61491828
</td>
</tr>
<tr>
<td>
141
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
59228.248104398146
</td>
<td>
59253.56092091435
</td>
<td>
61833113
</td>
</tr>
<tr>
<td>
142
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
59333.354320555554
</td>
<td>
59360.04917789352
</td>
<td>
65863007
</td>
</tr>
<tr>
<td>
143
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
59361.27141094908
</td>
<td>
59389.21600982639
</td>
<td>
65990217
</td>
</tr>
<tr>
<td>
144
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
59962.29642171296
</td>
<td>
59987.72269789352
</td>
<td>
178972056
</td>
</tr>
<tr>
<td>
145
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
59987.94029546296
</td>
<td>
60013.65147601852
</td>
<td>
192672058
</td>
</tr>
<tr>
<td>
146
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
60040.61506962963
</td>
<td>
60067.52998668981
</td>
<td>
199010078
</td>
</tr>
<tr>
<td>
147
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
60068.24064454861
</td>
<td>
60096.08825475694
</td>
<td>
199351997
</td>
</tr>
<tr>
<td>
148
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
60126.14391951389
</td>
<td>
60153.89601645833
</td>
<td>
201262105
</td>
</tr>
<tr>
<td>
149
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TESS-SPOC
</td>
<td>
60154.11129803241
</td>
<td>
60181.638605451386
</td>
<td>
201544704
</td>
</tr>
<tr>
<td>
150
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
59035.77808215236
</td>
<td>
59060.13918199111
</td>
<td>
96842434
</td>
</tr>
<tr>
<td>
151
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
59061.34752069507
</td>
<td>
59086.5975095816
</td>
<td>
99122009
</td>
</tr>
<tr>
<td>
152
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
59144.0141509953
</td>
<td>
59169.44469527109
</td>
<td>
99403814
</td>
</tr>
<tr>
<td>
153
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
59228.25022495072
</td>
<td>
59253.56271362631
</td>
<td>
99516461
</td>
</tr>
<tr>
<td>
154
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
59254.48632465489
</td>
<td>
59279.47936912952
</td>
<td>
99617468
</td>
</tr>
<tr>
<td>
155
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
59333.35434506787
</td>
<td>
59360.04877765756
</td>
<td>
99941936
</td>
</tr>
<tr>
<td>
156
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
59361.27099339198
</td>
<td>
59389.21542511648
</td>
<td>
100034118
</td>
</tr>
<tr>
<td>
157
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
59962.29387475597
</td>
<td>
59987.72441852046
</td>
<td>
113534134
</td>
</tr>
<tr>
<td>
158
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
59987.93506652862
</td>
<td>
60013.65264742589
</td>
<td>
118319607
</td>
</tr>
<tr>
<td>
159
</td>
<td>
HLSP
</td>
<td>
OPTICAL
</td>
<td>
TICA
</td>
<td>
60040.60865343083
</td>
<td>
60067.52993744984
</td>
<td>
128200817
</td>
</tr>
<tr>
<td>
160
</td>
<td>
HLSP
</td>
<td>
OPTICAL
</td>
<td>
TICA
</td>
<td>
60068.23363507073
</td>
<td>
60096.08778880769
</td>
<td>
137243062
</td>
</tr>
<tr>
<td>
161
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
60097.1688070111
</td>
<td>
60123.74055396067
</td>
<td>
147196500
</td>
</tr>
<tr>
<td>
162
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
60126.13638625294
</td>
<td>
60153.89563273499
</td>
<td>
150890395
</td>
</tr>
<tr>
<td>
163
</td>
<td>
HLSP
</td>
<td>
Optical
</td>
<td>
TICA
</td>
<td>
60154.10628673714
</td>
<td>
60181.63868136378
</td>
<td>
165587028
</td>
</tr>
<tr>
<td>
164
</td>
<td>
GALEX
</td>
<td>
UV
</td>
<td>
AIS
</td>
<td>
53970.45714120371
</td>
<td>
53970.458344907405
</td>
<td>
38561
</td>
</tr>
<tr>
<td>
165
</td>
<td>
GALEX
</td>
<td>
UV
</td>
<td>
AIS
</td>
<td>
53970.45714120371
</td>
<td>
53970.458344907405
</td>
<td>
38561
</td>
</tr>
</tbody>
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22
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<label class="nb-execution_count" id="cell-22-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-22-select">
cell type
<select form="cell-22" name="cell_type">
<option id="cell-22-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-22-source-label" class="nb-toolbar" hidden id="cell-22" name="cell-22">
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<textarea aria-labelledby="cell-22-source-label nb-source-label" class="nb-source" form="cell-22" id="cell-22-source" name="source" readonly rows="3">It's worth noting that even though we queried using the TESS Input Catalog, not all of the results are from the TESS mission. The TIC is a catalog like any other, and the MAST Archive resolves the input name to a location on the sky. Other missions that have observed the same region will also return results.
To search for results that are specific to TESS, or your mission of choice, see the 'by other criteria' section below.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>It's worth noting that even though we queried using the TESS Input Catalog, not all of the results are from the TESS mission. The TIC is a catalog like any other, and the MAST Archive resolves the input name to a location on the sky. Other missions that have observed the same region will also return results.
To search for results that are specific to TESS, or your mission of choice, see the 'by other criteria' section below.
</code></pre>
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<pre><code>
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3
</td>
<td class="nb-outputs" role="none">
<p>
It's worth noting that even though we queried using the TESS Input Catalog, not all of the results are from the TESS mission. The TIC is a catalog like any other, and the MAST Archive resolves the input name to a location on the sky. Other missions that have observed the same region will also return results.
</p>
<p>
To search for results that are specific to TESS, or your mission of choice, see the 'by other criteria' section below.
</p>
</td>
</tr>
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23
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<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-23-select">
cell type
<select form="cell-23" name="cell_type">
<option id="cell-23-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
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<td class="nb-toolbar" hidden role="none">
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<textarea aria-labelledby="cell-23-source-label nb-source-label" class="nb-source" form="cell-23" id="cell-23-source" name="source" readonly rows="7">### 3. By Other Criteria (with or without name/region) &lt;a id="crit"&gt;&lt;/a&gt;
To search for observations based on additonal parameters, you can use `query_criteria`. In a sense, this is a more powerful version of the tools above, as you can still search by coordinates and objectname; however, you can inculde additional desired criteria. You may also run the search without specifying a name or coordinates.
To perform the search, give your critera as keyword arguments. Valid criteria and their descriptions are listed [here](https://mast.stsci.edu/api/v0/_c_a_o_mfields.html). (Note that these are columns of the results tables we saw above!) Some relevant examples are: "filters", "t_exptime" (exposure time), instrument_name, "provenance_name", and "sequence_number".
Let's for TESS Sector 9 data with an exposure time between 1400 and 1500s.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">### 3. By Other Criteria (with or without name/region) &lt;a id="crit"&gt;&lt;/a&gt;</span>
To search for observations based on additonal parameters, you can use <span class="sb" title="Literal.String.Backtick">`query_criteria`</span>. In a sense, this is a more powerful version of the tools above, as you can still search by coordinates and objectname; however, you can inculde additional desired criteria. You may also run the search without specifying a name or coordinates.
To perform the search, give your critera as keyword arguments. Valid criteria and their descriptions are listed [<span class="nt" title="Name.Tag">here</span>](<span class="na" title="Name.Attribute">https://mast.stsci.edu/api/v0/_c_a_o_mfields.html</span>). (Note that these are columns of the results tables we saw above!) Some relevant examples are: "filters", "t_exptime" (exposure time), instrument_name, "provenance_name", and "sequence_number".
Let's for TESS Sector 9 data with an exposure time between 1400 and 1500s.
</code></pre>
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{}
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7
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<td class="nb-outputs" role="none">
<h3>
3. By Other Criteria (with or without name/region)
<a id="crit">
</a>
</h3>
<p>
To search for observations based on additonal parameters, you can use
<code>
query_criteria
</code>
. In a sense, this is a more powerful version of the tools above, as you can still search by coordinates and objectname; however, you can inculde additional desired criteria. You may also run the search without specifying a name or coordinates.
</p>
<p>
To perform the search, give your critera as keyword arguments. Valid criteria and their descriptions are listed
<a href="https://mast.stsci.edu/api/v0/_c_a_o_mfields.html">
here
</a>
. (Note that these are columns of the results tables we saw above!) Some relevant examples are: "filters", "t_exptime" (exposure time), instrument_name, "provenance_name", and "sequence_number".
</p>
<p>
Let's for TESS Sector 9 data with an exposure time between 1400 and 1500s.
</p>
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2024-02-02T04:08:11.453132+00:00
</time>
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<time datetime="2024-02-02T04:08:33.871+00:00">
2024-02-02T04:08:33.871791+00:00
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<textarea aria-labelledby="cell-24-source-label nb-source-label" class="nb-source" form="cell-24" id="cell-24-source" name="source" readonly rows="2">obsByCriteria = Observations.query_criteria(obs_collection=["TESS"], sequence_number=9,
t_exptime=[1400,1500])</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="n" title="Name">obsByCriteria</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">query_criteria</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">obs_collection</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"TESS"</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">sequence_number</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">9</span><span class="p" title="Punctuation">,</span>
<span class="n" title="Name">t_exptime</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">1400</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">1500</span><span class="p" title="Punctuation">])</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:08:11.452749Z', 'iopub.execute_input': '2024-02-02T04:08:11.453132Z', 'shell.execute_reply': '2024-02-02T04:08:33.871791Z', 'iopub.status.idle': '2024-02-02T04:08:33.872371Z'}}
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In 10 has
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outputs.
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<time datetime="2024-02-02T04:08:33.875+00:00">
2024-02-02T04:08:33.875870+00:00
</time>
</td>
<td class="nb-end" hidden id="cell-25-end" role="none">
<time datetime="2024-02-02T04:08:33.890+00:00">
2024-02-02T04:08:33.890867+00:00
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<textarea aria-labelledby="cell-25-source-label nb-source-label" class="nb-source" form="cell-25" id="cell-25-source" name="source" readonly rows="2">columns = ['target_name','s_ra', 's_dec', 't_exptime', 'obsid']
obsByCriteria[columns].show_in_notebook(display_length=5)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="n" title="Name">columns</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'target_name'</span><span class="p" title="Punctuation">,</span><span class="s1" title="Literal.String.Single">'s_ra'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'s_dec'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'t_exptime'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'obsid'</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">obsByCriteria</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">columns</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">show_in_notebook</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">display_length</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">5</span><span class="p" title="Punctuation">)</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:08:33.875624Z', 'iopub.execute_input': '2024-02-02T04:08:33.875870Z', 'shell.execute_reply': '2024-02-02T04:08:33.890867Z', 'iopub.status.idle': '2024-02-02T04:08:33.891613Z'}}
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11
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has 1 output
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62893522
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1
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TESS FFI
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62896721
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2
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TESS FFI
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<td>
110.78962946825075
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<td>
1425.599414
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<td>
62897785
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3
</td>
<td>
TESS FFI
</td>
<td>
75.9695971279556
</td>
<td>
-60.54438780588395
</td>
<td>
1425.599414
</td>
<td>
62898889
</td>
</tr>
<tr>
<td>
4
</td>
<td>
TESS FFI
</td>
<td>
100.0080806433491
</td>
<td>
-59.27494134673682
</td>
<td>
1425.599414
</td>
<td>
62898353
</td>
</tr>
<tr>
<td>
5
</td>
<td>
TESS FFI
</td>
<td>
163.9337788078188
</td>
<td>
-32.31080322363693
</td>
<td>
1425.599414
</td>
<td>
62894594
</td>
</tr>
<tr>
<td>
6
</td>
<td>
TESS FFI
</td>
<td>
133.67905448025425
</td>
<td>
-46.18239603008324
</td>
<td>
1425.599414
</td>
<td>
62896230
</td>
</tr>
<tr>
<td>
7
</td>
<td>
TESS FFI
</td>
<td>
173.83652057983417
</td>
<td>
-10.27752013936934
</td>
<td>
1425.599414
</td>
<td>
62892431
</td>
</tr>
<tr>
<td>
8
</td>
<td>
TESS FFI
</td>
<td>
162.4696250638618
</td>
<td>
-5.425652707919927
</td>
<td>
1425.599414
</td>
<td>
62892977
</td>
</tr>
<tr>
<td>
9
</td>
<td>
TESS FFI
</td>
<td>
151.37494470193366
</td>
<td>
-26.74595469529746
</td>
<td>
1425.599414
</td>
<td>
62895165
</td>
</tr>
<tr>
<td>
10
</td>
<td>
TESS FFI
</td>
<td>
148.8381670809981
</td>
<td>
-53.640196587551905
</td>
<td>
1425.599414
</td>
<td>
62895676
</td>
</tr>
<tr>
<td>
11
</td>
<td>
TESS FFI
</td>
<td>
157.27070350969674
</td>
<td>
-16.321715782665876
</td>
<td>
1425.599414
</td>
<td>
62891432
</td>
</tr>
<tr>
<td>
12
</td>
<td>
TESS FFI
</td>
<td>
135.13107661187195
</td>
<td>
-63.35224663118256
</td>
<td>
1425.599414
</td>
<td>
62897231
</td>
</tr>
<tr>
<td>
13
</td>
<td>
TESS FFI
</td>
<td>
72.28880568223647
</td>
<td>
-72.70228936448599
</td>
<td>
1425.599414
</td>
<td>
62899417
</td>
</tr>
<tr>
<td>
14
</td>
<td>
TESS FFI
</td>
<td>
157.48628713094715
</td>
<td>
-43.22571855614874
</td>
<td>
1425.599414
</td>
<td>
62894034
</td>
</tr>
<tr>
<td>
15
</td>
<td>
TESS FFI
</td>
<td>
169.0707180032819
</td>
<td>
-21.387226979894706
</td>
<td>
1425.599414
</td>
<td>
62891912
</td>
</tr>
</tbody>
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<label class="nb-execution_count" id="cell-26-source-label">
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In
</span>
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[
</span>
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</span>
<span aria-hidden="true">
]
</span>
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</td>
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</option>
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<textarea aria-labelledby="cell-26-source-label nb-source-label" class="nb-source" form="cell-26" id="cell-26-source" name="source" readonly rows="3">There's no limit on the number of filters you can apply in a search. It may be an interesting exercise for the reader to go through the example below and figure out what exactly we're searching for.
(Hint: check the [field descriptions](https://mast.stsci.edu/api/v0/_c_a_o_mfields.html))</textarea>
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<pre><span></span><code>There's no limit on the number of filters you can apply in a search. It may be an interesting exercise for the reader to go through the example below and figure out what exactly we're searching for.
(Hint: check the [<span class="nt" title="Name.Tag">field descriptions</span>](<span class="na" title="Name.Attribute">https://mast.stsci.edu/api/v0/_c_a_o_mfields.html</span>))
</code></pre>
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<pre><code>
{}
</code></pre>
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<td class="nb-loc" hidden id="cell-26-loc" role="none">
3
</td>
<td class="nb-outputs" role="none">
<p>
There's no limit on the number of filters you can apply in a search. It may be an interesting exercise for the reader to go through the example below and figure out what exactly we're searching for.
</p>
<p>
(Hint: check the
<a href="https://mast.stsci.edu/api/v0/_c_a_o_mfields.html">
field descriptions
</a>
)
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 27 cell-27-cell_type" class="cell code" data-index="27" data-loc="5" role="listitem">
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<a aria-describedby="nb-code-label nb-cell-label cell-27-loc nb-loc-label" aria-labelledby="nb-cell-label 27" class="nb-anchor" href="#27" id="27">
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<time datetime="2024-02-02T04:08:33.895+00:00">
2024-02-02T04:08:33.895656+00:00
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<time datetime="2024-02-02T04:08:34.055+00:00">
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<textarea aria-labelledby="cell-27-source-label nb-source-label" class="nb-source" form="cell-27" id="cell-27-source" name="source" readonly rows="5"># Make sure to run this cell, as the data is used in the sections that follow
exByCriteria = Observations.query_criteria(obs_collection=["HLA"], s_dec=[50,60],
calib_level=[3], proposal_pi="Mould*",
dataproduct_type="IMAGE", t_max=[49800,49820])</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Make sure to run this cell, as the data is used in the sections that follow</span>
<span class="n" title="Name">exByCriteria</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">query_criteria</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">obs_collection</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"HLA"</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">s_dec</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">50</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">60</span><span class="p" title="Punctuation">],</span>
<span class="n" title="Name">calib_level</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">3</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">proposal_pi</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"Mould*"</span><span class="p" title="Punctuation">,</span>
<span class="n" title="Name">dataproduct_type</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"IMAGE"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">t_max</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">49800</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">49820</span><span class="p" title="Punctuation">])</span>
</code></pre>
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<textarea aria-labelledby="cell-28-source-label nb-source-label" class="nb-source" form="cell-28" id="cell-28-source" name="source" readonly rows="2">columns = ['obs_collection', 'obs_id', 'target_name', 'filters', 'instrument_name', 'proposal_id']
exByCriteria[columns].show_in_notebook()</textarea>
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<pre><span></span><code><span class="n" title="Name">columns</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'obs_collection'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'obs_id'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'target_name'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'filters'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'instrument_name'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'proposal_id'</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">exByCriteria</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">columns</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">show_in_notebook</span><span class="p" title="Punctuation">()</span>
</code></pre>
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2
</td>
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<span>
Out
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[
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13
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</span>
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<span class="visually-hidden" id="cell-28-outputs-len">
has 1 output
</span>
<i>
Table length=4
</i>
<table class="table-striped table-bordered table-condensed" id="table140530652168416-898113">
<thead>
<tr>
<th>
idx
</th>
<th>
obs_collection
</th>
<th>
obs_id
</th>
<th>
target_name
</th>
<th>
filters
</th>
<th>
instrument_name
</th>
<th>
proposal_id
</th>
</tr>
</thead>
<tbody>
<tr>
<td>
0
</td>
<td>
HLA
</td>
<td>
hst_05766_04_wfpc2_f555w_pc
</td>
<td>
NGC5457-FLD2
</td>
<td>
F555W
</td>
<td>
WFPC2/PC
</td>
<td>
5766
</td>
</tr>
<tr>
<td>
1
</td>
<td>
HLA
</td>
<td>
hst_05766_04_wfpc2_f555w_wf
</td>
<td>
NGC5457-FLD2
</td>
<td>
F555W
</td>
<td>
WFPC2/WFC
</td>
<td>
5766
</td>
</tr>
<tr>
<td>
2
</td>
<td>
HLA
</td>
<td>
hst_05766_04_wfpc2_total_pc
</td>
<td>
NGC5457-FLD2
</td>
<td>
DETECTION
</td>
<td>
WFPC2/PC
</td>
<td>
5766
</td>
</tr>
<tr>
<td>
3
</td>
<td>
HLA
</td>
<td>
hst_05766_04_wfpc2_total_wf
</td>
<td>
NGC5457-FLD2
</td>
<td>
DETECTION
</td>
<td>
WFPC2/WFC
</td>
<td>
5766
</td>
</tr>
</tbody>
</table>
<style>
table.dataTable {clear: both; width: auto !important; margin: 0 !important;}
.dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{
display: inline-block; margin-right: 1em; }
.paginate_button { margin-right: 5px; }
</style>
<script>
var astropy_sort_num = function(a, b) {
var a_num = parseFloat(a);
var b_num = parseFloat(b);
if (isNaN(a_num) && isNaN(b_num))
return ((a < b) ? -1 : ((a > b) ? 1 : 0));
else if (!isNaN(a_num) && !isNaN(b_num))
return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));
else
return isNaN(a_num) ? -1 : 1;
}
require.config({paths: {
datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'
}});
require(["datatables"], function(){
console.log("$('#table140530652168416-898113').dataTable()");
jQuery.extend( jQuery.fn.dataTableExt.oSort, {
"optionalnum-asc": astropy_sort_num,
"optionalnum-desc": function (a,b) { return -astropy_sort_num(a, b); }
});
$('#table140530652168416-898113').dataTable({
order: [],
pageLength: 50,
lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],
pagingType: "full_numbers",
columnDefs: [{targets: [0], type: "optionalnum"}]
});
});
</script>
</fieldset>
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<textarea aria-labelledby="cell-29-source-label nb-source-label" class="nb-source" form="cell-29" id="cell-29-source" name="source" readonly rows="1">## Getting Associated Data Products</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Getting Associated Data Products</span>
</code></pre>
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<h2>
<a href="#getting-associated-data-products">
Getting Associated Data Products
</a>
</h2>
</td>
</tr>
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<textarea aria-labelledby="cell-30-source-label nb-source-label" class="nb-source" form="cell-30" id="cell-30-source" name="source" readonly rows="1">### Performing a Product Query
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### Performing a Product Query</span>
</code></pre>
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<h3>
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Performing a Product Query
</a>
</h3>
</td>
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<textarea aria-labelledby="cell-31-source-label nb-source-label" class="nb-source" form="cell-31" id="cell-31-source" name="source" readonly rows="5">Each observation returned from a MAST query can have one or more associated data products. For example, a JWST observation might return an [uncalibrated file](https://outerspace.stsci.edu/display/MASTDOCS/Supplemental+Products), [a guide-star file](https://jwst-docs.stsci.edu/jwst-observatory-characteristics/jwst-guide-stars), and the science data you're searching for.
You can input a table of observations or list of observation ids (&ldquo;obs_id&rdquo;) and `get_product_list` will return a table containing the associated data products.
Since we already have a list of observations, we can use that as the starting point for our query. To keep it simple, let's look at only the last observation from our search above.</textarea>
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<pre><span></span><code>Each observation returned from a MAST query can have one or more associated data products. For example, a JWST observation might return an [<span class="nt" title="Name.Tag">uncalibrated file</span>](<span class="na" title="Name.Attribute">https://outerspace.stsci.edu/display/MASTDOCS/Supplemental+Products</span>), [<span class="nt" title="Name.Tag">a guide-star file</span>](<span class="na" title="Name.Attribute">https://jwst-docs.stsci.edu/jwst-observatory-characteristics/jwst-guide-stars</span>), and the science data you're searching for.
You can input a table of observations or list of observation ids (&ldquo;obs_id&rdquo;) and <span class="sb" title="Literal.String.Backtick">`get_product_list`</span> will return a table containing the associated data products.
Since we already have a list of observations, we can use that as the starting point for our query. To keep it simple, let's look at only the last observation from our search above.
</code></pre>
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<p>
Each observation returned from a MAST query can have one or more associated data products. For example, a JWST observation might return an
<a href="https://outerspace.stsci.edu/display/MASTDOCS/Supplemental+Products">
uncalibrated file
</a>
,
<a href="https://jwst-docs.stsci.edu/jwst-observatory-characteristics/jwst-guide-stars">
a guide-star file
</a>
, and the science data you're searching for.
</p>
<p>
You can input a table of observations or list of observation ids (&ldquo;obs_id&rdquo;) and
<code>
get_product_list
</code>
will return a table containing the associated data products.
</p>
<p>
Since we already have a list of observations, we can use that as the starting point for our query. To keep it simple, let's look at only the last observation from our search above.
</p>
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</td>
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<time datetime="2024-02-02T04:08:34.385+00:00">
2024-02-02T04:08:34.385830+00:00
</time>
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-32-source-label nb-source-label" class="nb-source" form="cell-32" id="cell-32-source" name="source" readonly rows="5"># Let's select a small subset from our critera search above
newObsList = exByCriteria[-1:]
# Now we get the list of products associated with that observation
dataProducts = Observations.get_product_list(newObsList)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="c1" title="Comment.Single"># Let's select a small subset from our critera search above</span>
<span class="n" title="Name">newObsList</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">exByCriteria</span><span class="p" title="Punctuation">[</span><span class="o" title="Operator">-</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">:]</span>
<span class="c1" title="Comment.Single"># Now we get the list of products associated with that observation</span>
<span class="n" title="Name">dataProducts</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">get_product_list</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">newObsList</span><span class="p" title="Punctuation">)</span>
</code></pre>
</div>
</div>
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</button>
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</button>
<pre><code>
{'execution': {'iopub.status.busy': '2024-02-02T04:08:34.076132Z', 'iopub.execute_input': '2024-02-02T04:08:34.077176Z', 'shell.execute_reply': '2024-02-02T04:08:34.385830Z', 'iopub.status.idle': '2024-02-02T04:08:34.386412Z'}}
</code></pre>
</form>
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</td>
<td class="nb-loc" hidden id="cell-32-loc" role="none">
5
</td>
<td class="nb-outputs" role="none">
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In 14 has
0
outputs.
</span>
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</tr>
<tr aria-labelledby="nb-cell-label 33 cell-33-cell_type" class="cell code" data-index="33" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-code-label nb-cell-label cell-33-loc nb-loc-label" aria-labelledby="nb-cell-label 33" class="nb-anchor" href="#33" id="33">
33
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</td>
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<label class="nb-execution_count" id="cell-33-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
15
</span>
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]
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</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-33-select">
cell type
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<td class="nb-start" hidden id="cell-33-start" role="none">
<time datetime="2024-02-02T04:08:34.390+00:00">
2024-02-02T04:08:34.390999+00:00
</time>
</td>
<td class="nb-end" hidden id="cell-33-end" role="none">
<time datetime="2024-02-02T04:08:34.421+00:00">
2024-02-02T04:08:34.421949+00:00
</time>
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-33-source-label nb-source-label" class="nb-source" form="cell-33" id="cell-33-source" name="source" readonly rows="1">dataProducts.show_in_notebook(display_length=5)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="n" title="Name">dataProducts</span><span class="o" title="Operator">.</span><span class="n" title="Name">show_in_notebook</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">display_length</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">5</span><span class="p" title="Punctuation">)</span>
</code></pre>
</div>
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<button aria-controls="cell-33-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-33" name="metadata" onclick="openDialog()">
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<pre><code>
{'execution': {'iopub.status.busy': '2024-02-02T04:08:34.389397Z', 'iopub.execute_input': '2024-02-02T04:08:34.390999Z', 'iopub.status.idle': '2024-02-02T04:08:34.422613Z', 'shell.execute_reply': '2024-02-02T04:08:34.421949Z'}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-33-loc" role="none">
1
</td>
<td class="nb-outputs" role="none">
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</span>
<span aria-hidden="true">
[
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<span>
15
</span>
<span aria-hidden="true">
]
</span>
</legend>
<span class="visually-hidden" id="cell-33-outputs-len">
has 1 output
</span>
<i>
Table length=33
</i>
<table class="table-striped table-bordered table-condensed" id="table140530654246576-725600">
<thead>
<tr>
<th>
idx
</th>
<th>
obsID
</th>
<th>
obs_collection
</th>
<th>
dataproduct_type
</th>
<th>
obs_id
</th>
<th>
description
</th>
<th>
type
</th>
<th>
dataURI
</th>
<th>
productType
</th>
<th>
productGroupDescription
</th>
<th>
productSubGroupDescription
</th>
<th>
productDocumentationURL
</th>
<th>
project
</th>
<th>
prvversion
</th>
<th>
proposal_id
</th>
<th>
productFilename
</th>
<th>
size
</th>
<th>
parent_obsid
</th>
<th>
dataRights
</th>
<th>
calib_level
</th>
</tr>
</thead>
<tbody>
<tr>
<td>
0
</td>
<td>
25153181
</td>
<td>
HLA
</td>
<td>
image
</td>
<td>
hst_05766_04_wfpc2_f555w_wf_02
</td>
<td>
Preview-Full
</td>
<td>
S
</td>
<td>
mast:HLA/url/cgi-bin/preview.cgi?dataset=hst_05766_04_wfpc2_f555w_wf_02
</td>
<td>
PREVIEW
</td>
<td>
--
</td>
<td>
--
</td>
<td>
--
</td>
<td>
HLA
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
hst_05766_04_wfpc2_f555w_wf_02_drz.jpg
</td>
<td>
--
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
1
</td>
<td>
25153181
</td>
<td>
HLA
</td>
<td>
image
</td>
<td>
hst_05766_04_wfpc2_f555w_wf_02
</td>
<td>
HLA simple fits science image
</td>
<td>
S
</td>
<td>
mast:HLA/url/cgi-bin/getdata.cgi?dataset=hst_05766_04_wfpc2_f555w_wf_02_drz.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
DRZ
</td>
<td>
--
</td>
<td>
HLA
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
hst_05766_04_wfpc2_f555w_wf_02_drz.fits
</td>
<td>
10281600
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
2
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<td>
25579950
</td>
<td>
HLA
</td>
<td>
image
</td>
<td>
hst_05766_04_wfpc2_total_wf
</td>
<td>
HLA DAOPHOT Catalog
</td>
<td>
C
</td>
<td>
mast:HLA/url/cgi-bin/getdata.cgi?download=1&amp;filename=hst_05766_04_wfpc2_total_wf_daophot_trm.cat
</td>
<td>
CATALOG
</td>
<td>
Minimum Recommended Products
</td>
<td>
DAOPHOT
</td>
<td>
--
</td>
<td>
HLA
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
hst_05766_04_wfpc2_total_wf_daophot_trm.cat
</td>
<td>
--
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
3
</td>
</tr>
<tr>
<td>
3
</td>
<td>
25579950
</td>
<td>
HLA
</td>
<td>
image
</td>
<td>
hst_05766_04_wfpc2_total_wf
</td>
<td>
HLA SExtractor Catalog
</td>
<td>
C
</td>
<td>
mast:HLA/url/cgi-bin/getdata.cgi?download=1&amp;filename=hst_05766_04_wfpc2_total_wf_sexphot_trm.cat
</td>
<td>
CATALOG
</td>
<td>
Minimum Recommended Products
</td>
<td>
SEXPHOT
</td>
<td>
--
</td>
<td>
HLA
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
hst_05766_04_wfpc2_total_wf_sexphot_trm.cat
</td>
<td>
--
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
3
</td>
</tr>
<tr>
<td>
4
</td>
<td>
25579950
</td>
<td>
HLA
</td>
<td>
image
</td>
<td>
hst_05766_04_wfpc2_total_wf
</td>
<td>
Preview-Full
</td>
<td>
C
</td>
<td>
mast:HLA/url/cgi-bin/preview.cgi?dataset=hst_05766_04_wfpc2_total_wf
</td>
<td>
PREVIEW
</td>
<td>
--
</td>
<td>
--
</td>
<td>
--
</td>
<td>
HLA
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
hst_05766_04_wfpc2_total_wf_drz.jpg
</td>
<td>
--
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
3
</td>
</tr>
<tr>
<td>
5
</td>
<td>
25579950
</td>
<td>
HLA
</td>
<td>
image
</td>
<td>
hst_05766_04_wfpc2_total_wf
</td>
<td>
HLA simple fits science image
</td>
<td>
C
</td>
<td>
mast:HLA/url/cgi-bin/getdata.cgi?dataset=hst_05766_04_wfpc2_total_wf_drz.fits
</td>
<td>
SCIENCE
</td>
<td>
Minimum Recommended Products
</td>
<td>
DRZ
</td>
<td>
--
</td>
<td>
HLA
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
hst_05766_04_wfpc2_total_wf_drz.fits
</td>
<td>
30798720
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
3
</td>
</tr>
<tr>
<td>
6
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS C3M file - Calibrated exposure WFPC2
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_c3m.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
C3M
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_c3m.fits
</td>
<td>
207360
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
7
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS C3T file - Calibrated data WFPC2
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_c3t.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
C3T
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_c3t.fits
</td>
<td>
210240
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
8
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS CGR file - Calibrated data WFPC/FOC
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_cgr.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
CGR
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_cgr.fits
</td>
<td>
31680
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
9
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS CMH file
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402j_cmh.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
CMH
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402j_cmh.fits
</td>
<td>
17408
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
10
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS CMI file
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402j_cmi.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
CMI
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402j_cmi.fits
</td>
<td>
100864
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
11
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS CMJ file
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402j_cmj.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
CMJ
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402j_cmj.fits
</td>
<td>
2554880
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
12
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS DGR file - Raw data WFPC/FOC
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_dgr.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
DGR
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_dgr.fits
</td>
<td>
31680
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
13
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS FLT_HLET
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_flt_hlet.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
FLT_HLET
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_flt_hlet.fits
</td>
<td>
34560
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
14
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS Q0F file - Raw data quality WFPC/WFPC2/FOC/FOS/GHRS/HSP
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_q0f.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
Q0F
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_q0f.fits
</td>
<td>
5184000
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
15
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS Q0M file - Raw data quality WFPC2
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_q0m.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
Q0M
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_q0m.fits
</td>
<td>
5178240
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
16
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS Q1F file - Calibrated data quality WFPC/WFPC2/FOC/FOS/GHRS/HSP
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_q1f.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
Q1F
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_q1f.fits
</td>
<td>
103680
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
17
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS Q1M file - Calibrated data quality WFPC2
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_q1m.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
Q1M
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_q1m.fits
</td>
<td>
109440
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
18
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS SHM file - Engineering telemetry WFPC2
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_shm.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
SHM
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_shm.fits
</td>
<td>
34560
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
19
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS TRL file - Processing log
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_trl.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
TRL
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_trl.fits
</td>
<td>
28800
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
20
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS X0F file - Raw bias data WFPC/WFPC2/FOS
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_x0f.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
X0F
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_x0f.fits
</td>
<td>
103680
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
21
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS X0M file - Raw bias data WFPC2
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_x0m.fits
</td>
<td>
AUXILIARY
</td>
<td>
--
</td>
<td>
X0M
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_x0m.fits
</td>
<td>
109440
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
22
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS LOG file
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_log.txt
</td>
<td>
INFO
</td>
<td>
--
</td>
<td>
LOG
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_log.txt
</td>
<td>
62625
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
23
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
Preview-Full
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_c0f.jpg
</td>
<td>
PREVIEW
</td>
<td>
--
</td>
<td>
--
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_c0f.jpg
</td>
<td>
75344
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
24
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
Preview-Full
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_drw.jpg
</td>
<td>
PREVIEW
</td>
<td>
--
</td>
<td>
--
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_drw.jpg
</td>
<td>
1429826
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
3
</td>
</tr>
<tr>
<td>
25
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS C0F file - Calibrated exposure WFPC/WFPC2/FOC/FOS/GHRS/HSP
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_c0f.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
C0F
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_c0f.fits
</td>
<td>
10307520
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
26
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS C0M file - Calibrated exposure WFPC2
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_c0m.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
C0M
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_c0m.fits
</td>
<td>
10307520
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
27
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS C1F file - Calibrated exposure WFPC/FOC/FOS/GHRS/HSP
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_c1f.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
C1F
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_c1f.fits
</td>
<td>
5184000
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
28
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS C1M file
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_c1m.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
C1M
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_c1m.fits
</td>
<td>
5178240
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
29
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS D0F file - Raw exposure WFPC/WFPC2/FOC/FOS/GHRS/HSP
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_d0f.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
D0F
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_d0f.fits
</td>
<td>
5184000
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
30
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS D0M file - Raw exposure WFPC2
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_d0m.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
D0M
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
u2ms0402t_d0m.fits
</td>
<td>
5178240
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
1
</td>
</tr>
<tr>
<td>
31
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS DRW file
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_drw.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
DRW
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_drw.fits
</td>
<td>
145802880
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
3
</td>
</tr>
<tr>
<td>
32
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS FLT file - Calibrated exposure ACS/WFC3/STIS/COS
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_flt.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
FLT
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_flt.fits
</td>
<td>
25920000
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
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</td>
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<tr aria-labelledby="nb-cell-label 34 cell-34-cell_type" class="cell markdown" data-index="34" data-loc="5" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-34-loc nb-loc-label" aria-labelledby="nb-cell-label 34" class="nb-anchor" href="#34" id="34">
34
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-34-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-34-select">
cell type
<select form="cell-34" name="cell_type">
<option id="cell-34-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
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<form aria-labelledby="cell-34-source-label" class="nb-toolbar" hidden id="cell-34" name="cell-34">
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Run Cell
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<td class="nb-end" hidden id="cell-34-end" role="none">
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<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-34-source-label nb-source-label" class="nb-source" form="cell-34" id="cell-34-source" name="source" readonly rows="5">### Filtering the Data Products
After the data products have been retrieved, you can use `filter_products` to download only data products that meet your given criteria. Available filters are listed [here](https://mast.stsci.edu/api/v0/_productsfields.html). Some examples are: &ldquo;mrp_only&rdquo; (Minimum Recommended Products) and &ldquo;extension&rdquo; (file extension).
A note on filtering: each listed filter is joined with an AND, but each option within that filter is joined with an OR. For example, the search below will return any products that are 'science' type **and** have a calibration level of 2 **or** 3.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">### Filtering the Data Products</span>
After the data products have been retrieved, you can use <span class="sb" title="Literal.String.Backtick">`filter_products`</span> to download only data products that meet your given criteria. Available filters are listed [<span class="nt" title="Name.Tag">here</span>](<span class="na" title="Name.Attribute">https://mast.stsci.edu/api/v0/_productsfields.html</span>). Some examples are: &ldquo;mrp_only&rdquo; (Minimum Recommended Products) and &ldquo;extension&rdquo; (file extension).
A note on filtering: each listed filter is joined with an AND, but each option within that filter is joined with an OR. For example, the search below will return any products that are 'science' type <span class="gs" title="Generic.Strong">**and**</span> have a calibration level of 2 <span class="gs" title="Generic.Strong">**or**</span> 3.
</code></pre>
</div>
</div>
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metadata
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Close
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<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-34-loc" role="none">
5
</td>
<td class="nb-outputs" role="none">
<h3>
<a href="#filtering-the-data-products">
Filtering the Data Products
</a>
</h3>
<p>
After the data products have been retrieved, you can use
<code>
filter_products
</code>
to download only data products that meet your given criteria. Available filters are listed
<a href="https://mast.stsci.edu/api/v0/_productsfields.html">
here
</a>
. Some examples are: &ldquo;mrp_only&rdquo; (Minimum Recommended Products) and &ldquo;extension&rdquo; (file extension).
</p>
<p>
A note on filtering: each listed filter is joined with an AND, but each option within that filter is joined with an OR. For example, the search below will return any products that are 'science' type
<strong>
and
</strong>
have a calibration level of 2
<strong>
or
</strong>
3.
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 35 cell-35-cell_type" class="cell code" data-index="35" data-loc="2" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-code-label nb-cell-label cell-35-loc nb-loc-label" aria-labelledby="nb-cell-label 35" class="nb-anchor" href="#35" id="35">
35
</a>
</td>
<td class="nb-execution_count" role="none">
<label class="nb-execution_count" id="cell-35-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
16
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
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cell type
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<option value="markdown">
markdown
</option>
<option id="cell-35-cell_type" selected value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
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<textarea aria-labelledby="cell-35-source-label nb-source-label" class="nb-source" form="cell-35" id="cell-35-source" name="source" readonly rows="2">scienceProducts = Observations.filter_products(dataProducts, productType=["SCIENCE"],
calib_level=[2,3], mrp_only=False)</textarea>
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<pre><span></span><code><span class="n" title="Name">scienceProducts</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">filter_products</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">dataProducts</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">productType</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"SCIENCE"</span><span class="p" title="Punctuation">],</span>
<span class="n" title="Name">calib_level</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">2</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">3</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">mrp_only</span><span class="o" title="Operator">=</span><span class="kc" title="Keyword.Constant">False</span><span class="p" title="Punctuation">)</span>
</code></pre>
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<textarea aria-labelledby="cell-36-source-label nb-source-label" class="nb-source" form="cell-36" id="cell-36-source" name="source" readonly rows="1">scienceProducts.show_in_notebook(display_length=5)</textarea>
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<pre><span></span><code><span class="n" title="Name">scienceProducts</span><span class="o" title="Operator">.</span><span class="n" title="Name">show_in_notebook</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">display_length</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">5</span><span class="p" title="Punctuation">)</span>
</code></pre>
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</span>
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<span class="visually-hidden" id="cell-36-outputs-len">
has 1 output
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<i>
Table length=8
</i>
<table class="table-striped table-bordered table-condensed" id="table140530661874560-168850">
<thead>
<tr>
<th>
idx
</th>
<th>
obsID
</th>
<th>
obs_collection
</th>
<th>
dataproduct_type
</th>
<th>
obs_id
</th>
<th>
description
</th>
<th>
type
</th>
<th>
dataURI
</th>
<th>
productType
</th>
<th>
productGroupDescription
</th>
<th>
productSubGroupDescription
</th>
<th>
productDocumentationURL
</th>
<th>
project
</th>
<th>
prvversion
</th>
<th>
proposal_id
</th>
<th>
productFilename
</th>
<th>
size
</th>
<th>
parent_obsid
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<th>
dataRights
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<th>
calib_level
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<tbody>
<tr>
<td>
0
</td>
<td>
25153181
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<td>
HLA
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<td>
image
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HLA simple fits science image
</td>
<td>
S
</td>
<td>
mast:HLA/url/cgi-bin/getdata.cgi?dataset=hst_05766_04_wfpc2_f555w_wf_02_drz.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
DRZ
</td>
<td>
--
</td>
<td>
HLA
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
hst_05766_04_wfpc2_f555w_wf_02_drz.fits
</td>
<td>
10281600
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<td>
25579950
</td>
<td>
PUBLIC
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2
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1
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HLA
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<td>
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hst_05766_04_wfpc2_total_wf
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HLA simple fits science image
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<td>
C
</td>
<td>
mast:HLA/url/cgi-bin/getdata.cgi?dataset=hst_05766_04_wfpc2_total_wf_drz.fits
</td>
<td>
SCIENCE
</td>
<td>
Minimum Recommended Products
</td>
<td>
DRZ
</td>
<td>
--
</td>
<td>
HLA
</td>
<td>
--
</td>
<td>
5766
</td>
<td>
hst_05766_04_wfpc2_total_wf_drz.fits
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<td>
30798720
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<td>
25579950
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<td>
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3
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</td>
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S
</td>
<td>
mast:HST/product/u2ms0402t_c0f.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
C0F
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_c0f.fits
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<td>
10307520
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<td>
25579950
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PUBLIC
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2
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3
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<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS C0M file - Calibrated exposure WFPC2
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_c0m.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
C0M
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_c0m.fits
</td>
<td>
10307520
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
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<td>
4
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<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS C1F file - Calibrated exposure WFPC/FOC/FOS/GHRS/HSP
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_c1f.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
C1F
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_c1f.fits
</td>
<td>
5184000
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
5
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS C1M file
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_c1m.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
C1M
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_c1m.fits
</td>
<td>
5178240
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
<tr>
<td>
6
</td>
<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS DRW file
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_drw.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
DRW
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_drw.fits
</td>
<td>
145802880
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
3
</td>
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<tr>
<td>
7
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<td>
24556184
</td>
<td>
HST
</td>
<td>
image
</td>
<td>
u2ms0402t
</td>
<td>
DADS FLT file - Calibrated exposure ACS/WFC3/STIS/COS
</td>
<td>
S
</td>
<td>
mast:HST/product/u2ms0402t_flt.fits
</td>
<td>
SCIENCE
</td>
<td>
--
</td>
<td>
FLT
</td>
<td>
--
</td>
<td>
CALWFPC2
</td>
<td>
2.5.3 (Sep 4, 2008)
</td>
<td>
5766
</td>
<td>
u2ms0402t_flt.fits
</td>
<td>
25920000
</td>
<td>
25579950
</td>
<td>
PUBLIC
</td>
<td>
2
</td>
</tr>
</tbody>
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In
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[
</span>
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</span>
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]
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</td>
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<textarea aria-labelledby="cell-37-source-label nb-source-label" class="nb-source" form="cell-37" id="cell-37-source" name="source" readonly rows="7">## Downloading Products
Passing a table of products (like the one above) to `download_products` will download the entire table. You can also pass a list of Observation IDs (obs_id) if you know them.
`download_products` also allows you to filter data as you request the download. In the example below, we will only download the drizzled files (drz.fits).
Products will by default be downloaded into the current working directory, in a subdirectory called "mastDownload."&lt;br&gt;
The full local file paths will have the form "mastDownload/Mission/Observation ID/file."</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## Downloading Products</span>
Passing a table of products (like the one above) to <span class="sb" title="Literal.String.Backtick">`download_products`</span> will download the entire table. You can also pass a list of Observation IDs (obs_id) if you know them.
<span class="sb" title="Literal.String.Backtick">`download_products`</span> also allows you to filter data as you request the download. In the example below, we will only download the drizzled files (drz.fits).
Products will by default be downloaded into the current working directory, in a subdirectory called "mastDownload."&lt;br&gt;
The full local file paths will have the form "mastDownload/Mission/Observation ID/file."
</code></pre>
</div>
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7
</td>
<td class="nb-outputs" role="none">
<h2>
<a href="#downloading-products">
Downloading Products
</a>
</h2>
<p>
Passing a table of products (like the one above) to
<code>
download_products
</code>
will download the entire table. You can also pass a list of Observation IDs (obs_id) if you know them.
</p>
<p>
<code>
download_products
</code>
also allows you to filter data as you request the download. In the example below, we will only download the drizzled files (drz.fits).
</p>
<p>
Products will by default be downloaded into the current working directory, in a subdirectory called "mastDownload."
<br>
The full local file paths will have the form "mastDownload/Mission/Observation ID/file."
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 38 cell-38-cell_type" class="cell code" data-index="38" data-loc="5" role="listitem">
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</td>
<td class="nb-end" hidden id="cell-38-end" role="none">
<time datetime="2024-02-02T04:08:35.154+00:00">
2024-02-02T04:08:35.154994+00:00
</time>
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-38-source-label nb-source-label" class="nb-source" form="cell-38" id="cell-38-source" name="source" readonly rows="5"># This is the filtered download of the scienceProducts table
manifest = Observations.download_products(scienceProducts, extension=("drz.fits"))
# Uncomment below for "plain" download of the scienceProducts table
# manifest = Observations.download_products(scienceProducts)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="c1" title="Comment.Single"># This is the filtered download of the scienceProducts table</span>
<span class="n" title="Name">manifest</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">download_products</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">scienceProducts</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">extension</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"drz.fits"</span><span class="p" title="Punctuation">))</span>
<span class="c1" title="Comment.Single"># Uncomment below for "plain" download of the scienceProducts table</span>
<span class="c1" title="Comment.Single"># manifest = Observations.download_products(scienceProducts)</span>
</code></pre>
</div>
</div>
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{'execution': {'iopub.status.busy': '2024-02-02T04:08:34.458969Z', 'iopub.execute_input': '2024-02-02T04:08:34.459967Z', 'shell.execute_reply': '2024-02-02T04:08:35.154994Z', 'iopub.status.idle': '2024-02-02T04:08:35.155440Z'}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-38-loc" role="none">
5
</td>
<td class="nb-outputs" role="none">
<fieldset class="nb-outputs" data-outputs="2">
<legend aria-describedby="nb-outputs-desc" id="cell-38-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
18
</span>
<span aria-hidden="true">
]
</span>
</legend>
<span class="visually-hidden" id="cell-38-outputs-len">
has 2 outputs
</span>
<pre class="stdout">
<code>INFO: Found cached file ./mastDownload/HLA/hst_05766_04_wfpc2_f555w_wf_02/hst_05766_04_wfpc2_f555w_wf_02_drz.fits with expected size 10281600. [astroquery.query]
</code>
</pre>
<pre class="stdout">
<code>INFO: Found cached file ./mastDownload/HLA/hst_05766_04_wfpc2_total_wf/hst_05766_04_wfpc2_total_wf_drz.fits with expected size 30798720. [astroquery.query]
</code>
</pre>
</fieldset>
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<tr aria-labelledby="nb-cell-label 39 cell-39-cell_type" class="cell markdown" data-index="39" data-loc="1" role="listitem">
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<a aria-describedby="nb-markdown-label nb-cell-label cell-39-loc nb-loc-label" aria-labelledby="nb-cell-label 39" class="nb-anchor" href="#39" id="39">
39
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<span>
In
</span>
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[
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]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-39-select">
cell type
<select form="cell-39" name="cell_type">
<option id="cell-39-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
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<textarea aria-labelledby="cell-39-source-label nb-source-label" class="nb-source" form="cell-39" id="cell-39-source" name="source" readonly rows="1">Note: `download_products` includes caching by default. If you have downloaded the files before, they will not be downloaded again unless caching is turned off. This may cause issues if the data is updated and the filename remains the same!</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>Note: <span class="sb" title="Literal.String.Backtick">`download_products`</span> includes caching by default. If you have downloaded the files before, they will not be downloaded again unless caching is turned off. This may cause issues if the data is updated and the filename remains the same!
</code></pre>
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{}
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1
</td>
<td class="nb-outputs" role="none">
<p>
Note:
<code>
download_products
</code>
includes caching by default. If you have downloaded the files before, they will not be downloaded again unless caching is turned off. This may cause issues if the data is updated and the filename remains the same!
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 40 cell-40-cell_type" class="cell code" data-index="40" data-loc="1" role="listitem">
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<a aria-describedby="nb-code-label nb-cell-label cell-40-loc nb-loc-label" aria-labelledby="nb-cell-label 40" class="nb-anchor" href="#40" id="40">
40
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In
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cell type
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<option value="markdown">
markdown
</option>
<option id="cell-40-cell_type" selected value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
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<time datetime="2024-02-02T04:08:35.159+00:00">
2024-02-02T04:08:35.159154+00:00
</time>
</td>
<td class="nb-end" hidden id="cell-40-end" role="none">
<time datetime="2024-02-02T04:08:35.164+00:00">
2024-02-02T04:08:35.164934+00:00
</time>
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-40-source-label nb-source-label" class="nb-source" form="cell-40" id="cell-40-source" name="source" readonly rows="1">manifest.show_in_notebook()</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="n" title="Name">manifest</span><span class="o" title="Operator">.</span><span class="n" title="Name">show_in_notebook</span><span class="p" title="Punctuation">()</span>
</code></pre>
</div>
</div>
</td>
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<button aria-controls="cell-40-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-40" name="metadata" onclick="openDialog()">
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Close
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<pre><code>
{'execution': {'iopub.status.busy': '2024-02-02T04:08:35.158176Z', 'iopub.execute_input': '2024-02-02T04:08:35.159154Z', 'iopub.status.idle': '2024-02-02T04:08:35.165403Z', 'shell.execute_reply': '2024-02-02T04:08:35.164934Z'}}
</code></pre>
</form>
</dialog>
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1
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<span>
Out
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</span>
<span>
19
</span>
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</span>
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<span class="visually-hidden" id="cell-40-outputs-len">
has 1 output
</span>
<i>
Table length=2
</i>
<table class="table-striped table-bordered table-condensed" id="table140530661875280-245545">
<thead>
<tr>
<th>
idx
</th>
<th>
Local Path
</th>
<th>
Status
</th>
<th>
Message
</th>
<th>
URL
</th>
</tr>
</thead>
<tbody>
<tr>
<td>
0
</td>
<td>
./mastDownload/HLA/hst_05766_04_wfpc2_f555w_wf_02/hst_05766_04_wfpc2_f555w_wf_02_drz.fits
</td>
<td>
COMPLETE
</td>
<td>
None
</td>
<td>
None
</td>
</tr>
<tr>
<td>
1
</td>
<td>
./mastDownload/HLA/hst_05766_04_wfpc2_total_wf/hst_05766_04_wfpc2_total_wf_drz.fits
</td>
<td>
COMPLETE
</td>
<td>
None
</td>
<td>
None
</td>
</tr>
</tbody>
</table>
<style>
table.dataTable {clear: both; width: auto !important; margin: 0 !important;}
.dataTables_info, .dataTables_length, .dataTables_filter, .dataTables_paginate{
display: inline-block; margin-right: 1em; }
.paginate_button { margin-right: 5px; }
</style>
<script>
var astropy_sort_num = function(a, b) {
var a_num = parseFloat(a);
var b_num = parseFloat(b);
if (isNaN(a_num) && isNaN(b_num))
return ((a < b) ? -1 : ((a > b) ? 1 : 0));
else if (!isNaN(a_num) && !isNaN(b_num))
return ((a_num < b_num) ? -1 : ((a_num > b_num) ? 1 : 0));
else
return isNaN(a_num) ? -1 : 1;
}
require.config({paths: {
datatables: 'https://cdn.datatables.net/1.10.12/js/jquery.dataTables.min'
}});
require(["datatables"], function(){
console.log("$('#table140530661875280-245545').dataTable()");
jQuery.extend( jQuery.fn.dataTableExt.oSort, {
"optionalnum-asc": astropy_sort_num,
"optionalnum-desc": function (a,b) { return -astropy_sort_num(a, b); }
});
$('#table140530661875280-245545').dataTable({
order: [],
pageLength: 50,
lengthMenu: [[10, 25, 50, 100, 500, 1000, -1], [10, 25, 50, 100, 500, 1000, 'All']],
pagingType: "full_numbers",
columnDefs: [{targets: [0], type: "optionalnum"}]
});
});
</script>
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 41 cell-41-cell_type" class="cell markdown" data-index="41" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-41-loc nb-loc-label" aria-labelledby="nb-cell-label 41" class="nb-anchor" href="#41" id="41">
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<span>
In
</span>
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<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-41-select">
cell type
<select form="cell-41" name="cell_type">
<option id="cell-41-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-41-source-label" class="nb-toolbar" hidden id="cell-41" name="cell-41">
<fieldset>
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</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-41-start" role="none">
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<td class="nb-end" hidden id="cell-41-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-41-source-label nb-source-label" class="nb-source" form="cell-41" id="cell-41-source" name="source" readonly rows="1">The manifest returns useful information about the status of the files. You can find the local path, along with a status. This will either be COMPLETE, SKIPPED, or ERROR. If the status is ERROR, there will be additional information in the 'Message' column. The URL field includes a link to directly download the data.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>The manifest returns useful information about the status of the files. You can find the local path, along with a status. This will either be COMPLETE, SKIPPED, or ERROR. If the status is ERROR, there will be additional information in the 'Message' column. The URL field includes a link to directly download the data.
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-41-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-41" name="metadata" onclick="openDialog()">
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Close
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<pre><code>
{}
</code></pre>
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<td class="nb-loc" hidden id="cell-41-loc" role="none">
1
</td>
<td class="nb-outputs" role="none">
<p>
The manifest returns useful information about the status of the files. You can find the local path, along with a status. This will either be COMPLETE, SKIPPED, or ERROR. If the status is ERROR, there will be additional information in the 'Message' column. The URL field includes a link to directly download the data.
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 42 cell-42-cell_type" class="cell markdown" data-index="42" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-42-loc nb-loc-label" aria-labelledby="nb-cell-label 42" class="nb-anchor" href="#42" id="42">
42
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-42-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-42-select">
cell type
<select form="cell-42" name="cell_type">
<option id="cell-42-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-42-source-label" class="nb-toolbar" hidden id="cell-42" name="cell-42">
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</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-42-source-label nb-source-label" class="nb-source" form="cell-42" id="cell-42-source" name="source" readonly rows="1">## Displaying Data</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Displaying Data</span>
</code></pre>
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{}
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1
</td>
<td class="nb-outputs" role="none">
<h2>
<a href="#displaying-data">
Displaying Data
</a>
</h2>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 43 cell-43-cell_type" class="cell markdown" data-index="43" data-loc="3" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-43-loc nb-loc-label" aria-labelledby="nb-cell-label 43" class="nb-anchor" href="#43" id="43">
43
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<td class="nb-execution_count" hidden role="none">
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<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-43-select">
cell type
<select form="cell-43" name="cell_type">
<option id="cell-43-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-43-source-label" class="nb-toolbar" hidden id="cell-43" name="cell-43">
<fieldset>
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Run Cell
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<td class="nb-start" hidden id="cell-43-start" role="none">
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<td class="nb-end" hidden id="cell-43-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-43-source-label nb-source-label" class="nb-source" form="cell-43" id="cell-43-source" name="source" readonly rows="3">At this point the data is ready for analysis, and we are done querying the MAST Archive.
Below we take a look at the data files using `astropy.fits` and `matplotlib`.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>At this point the data is ready for analysis, and we are done querying the MAST Archive.
Below we take a look at the data files using <span class="sb" title="Literal.String.Backtick">`astropy.fits`</span> and <span class="sb" title="Literal.String.Backtick">`matplotlib`</span>.
</code></pre>
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<pre><code>
{}
</code></pre>
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<td class="nb-loc" hidden id="cell-43-loc" role="none">
3
</td>
<td class="nb-outputs" role="none">
<p>
At this point the data is ready for analysis, and we are done querying the MAST Archive.
</p>
<p>
Below we take a look at the data files using
<code>
astropy.fits
</code>
and
<code>
matplotlib
</code>
.
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 44 cell-44-cell_type" class="cell code" data-index="44" data-loc="7" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-code-label nb-cell-label cell-44-loc nb-loc-label" aria-labelledby="nb-cell-label 44" class="nb-anchor" href="#44" id="44">
44
</a>
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<td class="nb-execution_count" role="none">
<label class="nb-execution_count" id="cell-44-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
20
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-44-select">
cell type
<select form="cell-44" name="cell_type">
<option value="markdown">
markdown
</option>
<option id="cell-44-cell_type" selected value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-44-source-label" class="nb-toolbar" hidden id="cell-44" name="cell-44">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-44-start" role="none">
<time datetime="2024-02-02T04:08:35.169+00:00">
2024-02-02T04:08:35.169361+00:00
</time>
</td>
<td class="nb-end" hidden id="cell-44-end" role="none">
<time datetime="2024-02-02T04:08:35.191+00:00">
2024-02-02T04:08:35.191033+00:00
</time>
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-44-source-label nb-source-label" class="nb-source" form="cell-44" id="cell-44-source" name="source" readonly rows="7"># Get the filenames from the manifest
filename0 = manifest['Local Path'][0]
filename1 = manifest['Local Path'][1]
# Open the files using astropy.fits
file1 = fits.open(filename0)
file2 = fits.open(filename1)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="c1" title="Comment.Single"># Get the filenames from the manifest</span>
<span class="n" title="Name">filename0</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">manifest</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'Local Path'</span><span class="p" title="Punctuation">][</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">filename1</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">manifest</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'Local Path'</span><span class="p" title="Punctuation">][</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># Open the files using astropy.fits</span>
<span class="n" title="Name">file1</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">fits</span><span class="o" title="Operator">.</span><span class="n" title="Name">open</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">filename0</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">file2</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">fits</span><span class="o" title="Operator">.</span><span class="n" title="Name">open</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">filename1</span><span class="p" title="Punctuation">)</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:08:35.168379Z', 'iopub.execute_input': '2024-02-02T04:08:35.169361Z', 'iopub.status.idle': '2024-02-02T04:08:35.191838Z', 'shell.execute_reply': '2024-02-02T04:08:35.191033Z'}}
</code></pre>
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7
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In 20 has
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<time datetime="2024-02-02T04:08:35.196+00:00">
2024-02-02T04:08:35.196153+00:00
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<time datetime="2024-02-02T04:08:36.740+00:00">
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<textarea aria-labelledby="cell-45-source-label nb-source-label" class="nb-source" form="cell-45" id="cell-45-source" name="source" readonly rows="5">f, (ax1, ax2) = plt.subplots(1, 2)
f.set_figheight(5)
f.set_figwidth(12)
ax1.imshow(file1[0].data, cmap="inferno", norm=SymLogNorm(linthresh=0.03,vmin=0, vmax=1.5))
ax2.imshow(file2['SCI'].data, cmap="inferno", norm=SymLogNorm(linthresh=0.03,vmin=-0.01, vmax=1.5))</textarea>
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<pre><span></span><code><span class="n" title="Name">f</span><span class="p" title="Punctuation">,</span> <span class="p" title="Punctuation">(</span><span class="n" title="Name">ax1</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">ax2</span><span class="p" title="Punctuation">)</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">subplots</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">,</span> <span class="mi" title="Literal.Number.Integer">2</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">f</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_figheight</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">5</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">f</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_figwidth</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">12</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ax1</span><span class="o" title="Operator">.</span><span class="n" title="Name">imshow</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">file1</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">data</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">cmap</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"inferno"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">norm</span><span class="o" title="Operator">=</span><span class="n" title="Name">SymLogNorm</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">linthresh</span><span class="o" title="Operator">=</span><span class="mf" title="Literal.Number.Float">0.03</span><span class="p" title="Punctuation">,</span><span class="n" title="Name">vmin</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">vmax</span><span class="o" title="Operator">=</span><span class="mf" title="Literal.Number.Float">1.5</span><span class="p" title="Punctuation">))</span>
<span class="n" title="Name">ax2</span><span class="o" title="Operator">.</span><span class="n" title="Name">imshow</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">file2</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'SCI'</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">data</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">cmap</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"inferno"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">norm</span><span class="o" title="Operator">=</span><span class="n" title="Name">SymLogNorm</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">linthresh</span><span class="o" title="Operator">=</span><span class="mf" title="Literal.Number.Float">0.03</span><span class="p" title="Punctuation">,</span><span class="n" title="Name">vmin</span><span class="o" title="Operator">=-</span><span class="mf" title="Literal.Number.Float">0.01</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">vmax</span><span class="o" title="Operator">=</span><span class="mf" title="Literal.Number.Float">1.5</span><span class="p" title="Punctuation">))</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:08:35.195125Z', 'iopub.execute_input': '2024-02-02T04:08:35.196153Z', 'iopub.status.idle': '2024-02-02T04:08:36.741104Z', 'shell.execute_reply': '2024-02-02T04:08:36.740653Z'}}
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5
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<pre class="plain">&lt;matplotlib.image.AxesImage at 0x7fcfd81a2750&gt;</pre>
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">
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 46 cell-46-cell_type" class="cell markdown" data-index="46" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-46-loc nb-loc-label" aria-labelledby="nb-cell-label 46" class="nb-anchor" href="#46" id="46">
46
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-46-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-46-select">
cell type
<select form="cell-46" name="cell_type">
<option id="cell-46-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-46-source-label" class="nb-toolbar" hidden id="cell-46" name="cell-46">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-46-start" role="none">
</td>
<td class="nb-end" hidden id="cell-46-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-46-source-label nb-source-label" class="nb-source" form="cell-46" id="cell-46-source" name="source" readonly rows="1">We see the same region of the sky, with subtle differences caused by the differing filters.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>We see the same region of the sky, with subtle differences caused by the differing filters.
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-46-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-46" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-46-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-46-loc" role="none">
1
</td>
<td class="nb-outputs" role="none">
<p>
We see the same region of the sky, with subtle differences caused by the differing filters.
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 47 cell-47-cell_type" class="cell markdown" data-index="47" data-loc="2" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-47-loc nb-loc-label" aria-labelledby="nb-cell-label 47" class="nb-anchor" href="#47" id="47">
47
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-47-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-47-select">
cell type
<select form="cell-47" name="cell_type">
<option id="cell-47-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-47-source-label" class="nb-toolbar" hidden id="cell-47" name="cell-47">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-47-start" role="none">
</td>
<td class="nb-end" hidden id="cell-47-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-47-source-label nb-source-label" class="nb-source" form="cell-47" id="cell-47-source" name="source" readonly rows="2">## Further Reading
Full documentation on `astroquery.mast` can be found [here](https://astroquery.readthedocs.io/en/latest/mast/mast.html).</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## Further Reading</span>
Full documentation on <span class="sb" title="Literal.String.Backtick">`astroquery.mast`</span> can be found [<span class="nt" title="Name.Tag">here</span>](<span class="na" title="Name.Attribute">https://astroquery.readthedocs.io/en/latest/mast/mast.html</span>).
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-47-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-47" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-47-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-47-loc" role="none">
2
</td>
<td class="nb-outputs" role="none">
<h2>
<a href="#further-reading">
Further Reading
</a>
</h2>
<p>
Full documentation on
<code>
astroquery.mast
</code>
can be found
<a href="https://astroquery.readthedocs.io/en/latest/mast/mast.html">
here
</a>
.
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 48 cell-48-cell_type" class="cell markdown" data-index="48" data-loc="7" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-48-loc nb-loc-label" aria-labelledby="nb-cell-label 48" class="nb-anchor" href="#48" id="48">
48
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-48-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-48-select">
cell type
<select form="cell-48" name="cell_type">
<option id="cell-48-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-48-source-label" class="nb-toolbar" hidden id="cell-48" name="cell-48">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-48-start" role="none">
</td>
<td class="nb-end" hidden id="cell-48-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-48-source-label nb-source-label" class="nb-source" form="cell-48" id="cell-48-source" name="source" readonly rows="7">## About this Notebook
For additonal questions, comments, or feedback, please email `archive@stsci.edu`.
**Authors:** Thomas Dutkiewicz, Scott Fleming &lt;br&gt;
**Keywords:** MAST, astroquery &lt;br&gt;
**Latest update** Oct 2022 &lt;br&gt;
**Next Review:** Apr 2023</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## About this Notebook</span>
For additonal questions, comments, or feedback, please email <span class="sb" title="Literal.String.Backtick">`archive@stsci.edu`</span>.
<span class="gs" title="Generic.Strong">**Authors:**</span> Thomas Dutkiewicz, Scott Fleming &lt;br&gt;
<span class="gs" title="Generic.Strong">**Keywords:**</span> MAST, astroquery &lt;br&gt;
<span class="gs" title="Generic.Strong">**Latest update**</span> Oct 2022 &lt;br&gt;
<span class="gs" title="Generic.Strong">**Next Review:**</span> Apr 2023
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-48-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-48" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-48-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-48-loc" role="none">
7
</td>
<td class="nb-outputs" role="none">
<h2>
<a href="#about-this-notebook">
About this Notebook
</a>
</h2>
<p>
For additonal questions, comments, or feedback, please email
<code>
archive@stsci.edu
</code>
.
</p>
<p>
<strong>
Authors:
</strong>
Thomas Dutkiewicz, Scott Fleming
<br>
<strong>
Keywords:
</strong>
MAST, astroquery
<br>
<strong>
Latest update
</strong>
Oct 2022
<br>
<strong>
Next Review:
</strong>
Apr 2023
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 49 cell-49-cell_type" class="cell markdown" data-index="49" data-loc="2" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-49-loc nb-loc-label" aria-labelledby="nb-cell-label 49" class="nb-anchor" href="#49" id="49">
49
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-49-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-49-select">
cell type
<select form="cell-49" name="cell_type">
<option id="cell-49-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-49-source-label" class="nb-toolbar" hidden id="cell-49" name="cell-49">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-49-start" role="none">
</td>
<td class="nb-end" hidden id="cell-49-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-49-source-label nb-source-label" class="nb-source" form="cell-49" id="cell-49-source" name="source" readonly rows="2">[Top of Page](#top)
&lt;img style="float: right;" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" alt="Space Telescope Logo" width="200px"/&gt; </textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>[<span class="nt" title="Name.Tag">Top of Page</span>](<span class="na" title="Name.Attribute">#top</span>)
&lt;img style="float: right;" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" alt="Space Telescope Logo" width="200px"/&gt;
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-49-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-49" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-49-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-49-loc" role="none">
2
</td>
<td class="nb-outputs" role="none">
<p>
<a href="#top">
Top of Page
</a>
<img alt="Space Telescope Logo" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" style="float: right;" width="200px">
</p>
</td>
</tr>
</tbody>
</table>
<table class="nb-cells-footer" hidden>
<tbody>
<tr class="total">
<th scope="row">
all cells
</th>
<th scope="row">
count
</th>
<td class="nb-ordered">
unexecuted
</td>
<td class="nb-cell_type">
49
</td>
<td class="nb-source">
</td>
<td class="nb-outputs">
16
</td>
<td class="nb-toolbar">
</td>
<td class="nb-metadata">
</td>
<td class="nb-loc">
140
</td>
</tr>
<tr class="code">
<th scope="row">
code cells
</th>
<th scope="row">
count
</th>
<td class="nb-ordered">
unexecuted
</td>
<td class="nb-cell_type">
49
</td>
<td class="nb-source">
</td>
<td class="nb-outputs">
16
</td>
<td class="nb-toolbar">
</td>
<td class="nb-metadata">
</td>
<td class="nb-loc">
140
</td>
</tr>
<tr class="markdown">
<th scope="row">
markdown cells
</th>
<th scope="row">
count
</th>
<td class="nb-ordered">
</td>
<td class="nb-cell_type">
49
</td>
<td class="nb-source">
</td>
<td class="nb-outputs">
</td>
<td class="nb-toolbar">
</td>
<td class="nb-metadata">
</td>
<td class="nb-loc">
140
</td>
</tr>
</tbody>
</table>
<dialog id="nb-metadata">
<form method="dialog">
<button formmethod="dialog">
Close
</button>
{"language_info": {"name": "python", "version": "3.12.1", "mimetype": "text/x-python", "codemirror_mode": {"name": "ipython", "version": 3}, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py"}}
</form>
</dialog>
<form aria-labelledby="nb-notebook-label" id="notebook" name="notebook">
<label>
<input id="nb-edit-mode" name="edit" type="checkbox">
edit mode
</label>
<button aria-controls="nb-metadata" onclick="openDialog()">
metadata
</button>
<button type="submit">
Run
</button>
</form>
</main>
<section id="nb-dialogs">
<details>
<summary onclick="openDialogs()">
settings, help, &amp; diagnostics
</summary>
</details>
<dialog id="nb-help">
<h1>
<a href="#notebook-help">
notebook help
</a>
</h1>
<form>
<button formmethod="dialog">
close
</button>
<h2 id="nb-defs-label">
<a href="#nb-defs-label">
definitions
</a>
</h2>
<dl aria-labelledby="nb-defs-label">
<dt id="nb-notebook-label">
notebook
</dt>
<dd>
a collections of
<a href="#nb-cells-label">
cells
</a>
</dd>
<dd>
<dl aria-labelledby="nb-notebook-label nb-defs-label">
<dt id="nb-root-metadata-label">
metadata
</dt>
<dd id="nb-root-metadata-desc">
Notebook root-level metadata.
</dd>
<dd>
<dl>
<dt id="nb-kernelspec-label">
kernelspec
</dt>
<dd>
Kernel information.
</dd>
<dd>
<dl aria-labelledby="nb-kernelspec-label">
<dt>
kernel name
</dt>
<dd>
Name of the kernel specification.
</dd>
<dt>
display name
</dt>
<dd>
Name to display in UI.
</dd>
</dl>
</dd>
<dt id="nb-language_info-label">
language info
</dt>
<dd>
Kernel information.
</dd>
<dd>
<dl aria-labelledby="nb-language_info-label">
<dt>
programming language
</dt>
<dd>
The programming language which this kernel runs.
</dd>
<dt>
<a>
codemirror mode
</a>
</dt>
<dd>
The codemirror mode to use for code in this language.
</dd>
<dt>
file extension
</dt>
<dd>
The file extension for files in this language.
</dd>
<dt>
mimetype
</dt>
<dd>
The mimetype corresponding to files in this language.
</dd>
<dt>
pygments lexer
</dt>
<dd>
The pygments lexer to use for code in this language.
</dd>
</dl>
</dd>
<dt>
title
</dt>
<dd>
The title of the notebook document
</dd>
<dt>
authors
</dt>
<dd>
The author(s) of the notebook document
</dd>
</dl>
</dd>
<dt id="nb-cells-label">
cells
</dt>
<dd id="nb-cells-desc">
Array of cells of the current notebook.
</dd>
</dl>
</dd>
<dt id="nb-cell-label">
cell
</dt>
<dd>
<dl aria-labelledby="nb-cell-label nb-defs-label">
<dt id="nb-index-label">
index
</dt>
<dd id="nb-index-desc">
the ordinal location of the cell in the notebook, counting begins from 1.
</dd>
<dt id="nb-source-label">
source
</dt>
<dd id="nb-source-desc">
Contents of the cell, represented as an array of lines.
</dd>
<dt id="nb-metadata-label">
metadata
</dt>
<dd id="nb-metadata-desc">
Cell-level metadata.
</dd>
<dd>
<dl aria-labelledby="nb-metadata-label">
<dt>
name
</dt>
<dd>
</dd>
<dt>
tags
</dt>
<dd>
</dd>
<dt id="nb-jupyter-label">
jupyter
</dt>
<dd id="nb-jupyter-desc">
Official Jupyter Metadata for Raw Cells
</dd>
<dd>
<dl>
<dt>
source hidden
</dt>
<dd>
Whether the source is hidden.
</dd>
<dt>
outputs hidden
</dt>
<dd>
Whether the outputs are hidden.
</dd>
<dt id="nb-execution-label">
execution
</dt>
<dd id="nb-execution-desc">
Execution time for the code in the cell. This tracks time at which messages are received from iopub or shell channels
</dd>
<dd>
<dl aria-labelledby="nb-execution-label">
<dt>
execute input
</dt>
<dd>
{'description': "header.date (in ISO 8601 format) of iopub channel's execute_input message. It indicates the time at which the kernel broadcasts an execute_input message to connected frontends", 'type': 'string'}
</dd>
<dt>
execute reply
</dt>
<dd>
</dd>
</dl>
</dd>
<dt>
collapsed
</dt>
<dd>
Whether the cell's output is collapsed/expanded.
</dd>
<dt>
scrolled
</dt>
<dd>
Whether the cell's output is scrolled, unscrolled, or autoscrolled.
</dd>
</dl>
</dd>
</dl>
</dd>
<dt id="nb-cell_type-label">
cell type
</dt>
<dd>
the are three kinds of cells
<dl aria-labelledby="nb-cell_type-label">
<dt id="nb-code-label">
code
</dt>
<dd id="nb-code-desc">
Notebook code cell.
</dd>
<dt id="nb-markdown-label">
markdown
</dt>
<dd id="nb-markdown-desc">
Notebook markdown cell.
</dd>
<dt id="nb-raw-label">
raw
</dt>
<dd id="nb-raw-desc">
Notebook raw nbconvert cell.
</dd>
</dl>
</dd>
<dd id="nb-cell_type-desc">
String identifying the type of cell.
</dd>
<dt id="nb-execution_count-label">
execution count
</dt>
<dd id="nb-execution_count-desc">
The code cell's prompt number. Will be null if the cell has not been run.
</dd>
<dt id="nb-outputs-label">
outputs
</dt>
<dd id="nb-outputs-desc">
Execution, display, or stream outputs.
</dd>
<dt id="nb-attachments-label">
attachments
</dt>
<dd id="nb-attachments-desc">
Media attachments (e.g. inline images), stored as mimebundle keyed by filename.
</dd>
<dt id="nb-loc-label">
lines of code
</dt>
<dd>
lines of code in the cell, including whitespace.
</dd>
</dl>
</dd>
<dt id="nb-toolbar-label">
toolbar
</dt>
<dd>
composite widgets that allow for arrow key navigation
</dd>
</dl>
</form>
</dialog>
<button accesskey="?" aria-controls="nb-help" onclick="openDialog()">
help
</button>
</section>
<footer>
<p>
By STScI
</p>
<p>
&copy; Copyright 2022-2024
</p>
<details class="log">
<summary>
<span>
activity log
</span>
</summary>
</details>
<table aria-live="polite">
<tbody>
<tr>
<th hidden>
time
</th>
<td>
message
</td>
</tr>
</tbody>
</table>
<a accesskey="0" href="#TOP">
skip to top
</a>
</footer>
</body>
</html>
" width="100%">
</iframe>
<iframe height="600" srcdoc="<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta content="width=device-width, initial-scale=1.0" name="viewport">
<title>
Notebook
</title>
<meta content="light" name="color-scheme">
<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js">
</script>
<style type="text/css">
/* # accessible css configurations
we define these high level variables to allow for configuration during use and testing.
we are not sure what the preferred default values are for these any accessible notebooks features.
the variables specify critical degrees of freedom in the notebook style.
## notebook interface specific css variables
*/
:root {
--nb-focus-width: 3px;
--nb-accent-color: auto;
--nb-background-color-dark: #2b2a33;
--nb-background-color-light: #FFFFFF;
--nb-margin: 5%;
--nb-font-size: 16px;
--nb-font-family: serif;
--nb-line-height: 1.5;
}
/* map css variables to css properties on the `body` elements.
this mapping turns out css variables into user interfaces. */
body {
font-size: var(--nb-font-size);
font-family: var(--nb-font-family);
accent-color: var(--nb-accent-color);
margin-left: var(--nb-margin);
margin-right: var(--nb-margin);
line-height: var(--nb-line-height);
width: calc(100% - 2*var(--nb-margin));
}
/* ## notebook settings
the notebook settings interface connects configurable css features to the user.
we use dialog elements to represents an element that should be hidden by default.
it turns out there are two ways to layout `dialog` elements.
they can be used as modals or block elements.
we extract this dual purpose to provide both configurations to the user.
*/
dialog form>* {
display: block;
}
#nb-dialogs details[open]~dialog {
position: relative;
}
/* ## wcag accessibility guidelines as a feature
flexibility is the future of assistive interfaces.
it is not in the purvey of privileged developers and inaccessible systems to decide what priority accessibility is.
it is the fucking priority.
in this implementation, we want to satisfy broad user needs by making a lot of features configurable.
one configurable feature is the accessibility priority.
experienced developers may need less assistive features as they spend more time with an interface.
### buttons sizes
WCAG 2.1 defines a huge stinking AAA hit size for buttons. its beautiful.
/* satisfy AAA 2.5.5 */
.wcag-aaa button,
.wcag-aaa input[type=checkbox] {
min-height: 44px;
min-width: 44px;
}
.wcag-aaa a {
font-size: max(44px, var(--nb-font-size));
}
/* WCAG 2.2 clarified a AA button size that is used to style checkboxes and buttons. */
/* satisfy AA 2.5.8 minimum target requirement */
.wcag-aa button,
.wcag-aa input[type=checkbox] {
min-height: 24px;
min-width: 24px;
}
.wcag-aa a {
font-size: max(24px, var(--nb-font-size));
}
/* ### native textareas for AAA priority.
our AAA priority choices often ensure that third party libraries can not compromise the accessibility of notebook.
the design decisions are focused native elements so that the assistive technology users have consistent expectations and outcomes.
*/
.markdown textarea[name=source]+[role=group],
.wcag-a textarea[name=source],
.wcag-aa textarea[name=source],
.wcag-aaa .markdown textarea[name=source],
/* this group selector is very ambiguous */
.wcag-aaa textarea[name=source]+[role=group] {
display: none;
}
/* this is only needed for the lsit template variant */
ol#cells {
margin: unset;
padding: unset;
}
li.cell {
list-style-type: none;
}
/* ## notebook components layout */
td[role="none"]:not([hidden]) {
display: unset;
}
table {
border-spacing: unset;
}
main>.cell,
#cells .cell,
#cells > tbody {
display: flex;
flex-direction: column;
}
ol[reversed]#cells,
[reversed]#cells tbody {
flex-direction: column-reverse;
}
/* hide the visual output area when there are no outputs.
assistive technologies will recieve an audible notification that there are not outputs. */
fieldset[data-outputs="0"] {
display: none;
}
/* ## modifications to html defaults
### focus
there nothing to be said about this topic that [sara soueidan](https://www.sarasoueidan.com/blog/focus-indicators/) hasn't said.
we start with her [universal focus recommendation](https://www.sarasoueidan.com/blog/focus-indicators/#a-%E2%80%98universal%E2%80%99-focus-indicator).
*/
.cell:focus-within,
:focus-visible {
outline: max(var(--nb-focus-width), 1px) solid;
box-shadow: 0 0 0 calc(2 * max(var(--nb-focus-width), 1px));
}
/* on firefox, the input and output become interactive when there is overflow. chrome fixed this recently find reference.*/
textarea[name=source],
.cell>td>details {
overflow: auto;
min-width: 0;
}
#nb-settings li::marker,
summary[inert]::marker {
content: "";
}
/* ### inheriting styles */
input,
select,
button {
font-family: inherit;
font-size: inherit;
}
textarea {
font-family: monospace;
font-size: inherit;
line-height: inherit;
overflow: auto;
color: unset;
}
textarea[name=source] {
box-sizing: border-box;
width: 100%;
resize: vertical;
}
/* align checkboxes with buttons */
input[type="checkbox"] {
vertical-align: middle;
}
/* ## custom class selectors */
#nb-dialogs details:not([open])~dialog:not([open]):not(:focus-within):not(:active),
/* legend:not(:focus-within):not(:active), */
details.log:not([open])+table,
.visually-hidden:not(:focus-within):not(:active) {
clip: rect(0 0 0 0);
clip-path: inset(50%);
height: 1px;
overflow: hidden;
position: absolute;
white-space: nowrap;
width: 1px;
}
</style>
<style id="nb-light-theme" media="screen" type="text/css">
/** accessible pygments github-light-high-contrast generated by __main__**/
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
.hll { background-color: #0969da4a }
.c { color: #66707b } /* Comment */
.err { color: #a0111f } /* Error */
.k { color: #a0111f } /* Keyword */
.l { color: #702c00 } /* Literal */
.n { color: #622cbc } /* Name */
.o { color: #024c1a } /* Operator */
.p { color: #24292f } /* Punctuation */
.ch { color: #66707b } /* Comment.Hashbang */
.cm { color: #66707b } /* Comment.Multiline */
.cp { color: #66707b } /* Comment.Preproc */
.cpf { color: #66707b } /* Comment.PreprocFile */
.c1 { color: #66707b } /* Comment.Single */
.cs { color: #66707b } /* Comment.Special */
.gd { color: #023b95 } /* Generic.Deleted */
.ge { font-style: italic } /* Generic.Emph */
.gr { color: #a0111f } /* Generic.Error */
.gh { color: #023b95 } /* Generic.Heading */
.gs { font-weight: bold } /* Generic.Strong */
.gu { color: #023b95 } /* Generic.Subheading */
.kc { color: #023b95 } /* Keyword.Constant */
.kd { color: #a0111f } /* Keyword.Declaration */
.kn { color: #a0111f } /* Keyword.Namespace */
.kp { color: #a0111f } /* Keyword.Pseudo */
.kr { color: #a0111f } /* Keyword.Reserved */
.kt { color: #a0111f } /* Keyword.Type */
.ld { color: #702c00 } /* Literal.Date */
.m { color: #702c00 } /* Literal.Number */
.s { color: #023b95 } /* Literal.String */
.na { color: #702c00 } /* Name.Attribute */
.nb { color: #702c00 } /* Name.Builtin */
.nc { color: #023b95 } /* Name.Class */
.no { color: #023b95 } /* Name.Constant */
.nd { color: #702c00 } /* Name.Decorator */
.ni { color: #024c1a } /* Name.Entity */
.ne { color: #622cbc } /* Name.Exception */
.nf { color: #023b95 } /* Name.Function */
.nl { color: #702c00 } /* Name.Label */
.nn { color: #24292f } /* Name.Namespace */
.nx { color: #622cbc } /* Name.Other */
.py { color: #023b95 } /* Name.Property */
.nt { color: #024c1a } /* Name.Tag */
.nv { color: #702c00 } /* Name.Variable */
.ow { color: #622cbc } /* Operator.Word */
.pm { color: #24292f } /* Punctuation.Marker */
.w { color: #24292f } /* Text.Whitespace */
.mb { color: #702c00 } /* Literal.Number.Bin */
.mf { color: #702c00 } /* Literal.Number.Float */
.mh { color: #702c00 } /* Literal.Number.Hex */
.mi { color: #702c00 } /* Literal.Number.Integer */
.mo { color: #702c00 } /* Literal.Number.Oct */
.sa { color: #023b95 } /* Literal.String.Affix */
.sb { color: #023b95 } /* Literal.String.Backtick */
.sc { color: #023b95 } /* Literal.String.Char */
.dl { color: #023b95 } /* Literal.String.Delimiter */
.sd { color: #023b95 } /* Literal.String.Doc */
.s2 { color: #023b95 } /* Literal.String.Double */
.se { color: #023b95 } /* Literal.String.Escape */
.sh { color: #023b95 } /* Literal.String.Heredoc */
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<li>
<a href="#beginner-zcut-and-astroquery-tutorial">
Beginner: Zcut and Astroquery Tutorial
</a>
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<a href="#goals">
Goals:
</a>
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Table of Contents
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Import Statements
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Image File Cutouts
</a>
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Set up coordinates
</a>
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<a href="#query-for-available-surveys">
Query for Available Surveys
</a>
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<a href="#downloading-image-cutouts">
Downloading Image Cutouts
</a>
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</ol>
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<a href="#getting-fits-cutouts">
Getting FITS cutouts
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For further reading
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<textarea aria-labelledby="cell-1-source-label nb-source-label" class="nb-source" form="cell-1" id="cell-1-source" name="source" readonly rows="23"># Beginner: Zcut and Astroquery Tutorial
This notebook is a beginner tutorial on the Zcut feature of the MAST astroquery interface. Zcut allows you to request cutouts &mdash; either as `.fits` or image files &mdash; from various deep field surveys. The list of supported deep field surveys can be found here: https://mast.stsci.edu/zcut/
## Goals:
In this tutorial, you'll learn to:
1. Use RA/Dec coordinates to search for surveys containing your target
2. Create a cutout from deep field surveys
3. Download, process, and display:
* image file cutouts
* `.fits` cutouts
Let's get started!
## Table of Contents
* [Import Statements](#Import-Statements)
* [Image File Cutouts](#Image-File-Cutouts)
* [Set up coordinates](#Set-up-coordinates)
* [Query for Available Surveys](#Query-for-Available-Surveys)
* [Getting Cutouts](#Getting-Cutouts)
* [FITS cutouts](#Getting-FITS-cutouts)</textarea>
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<pre><span></span><code><span class="gh" title="Generic.Heading"># Beginner: Zcut and Astroquery Tutorial</span>
This notebook is a beginner tutorial on the Zcut feature of the MAST astroquery interface. Zcut allows you to request cutouts &mdash; either as <span class="sb" title="Literal.String.Backtick">`.fits`</span> or image files &mdash; from various deep field surveys. The list of supported deep field surveys can be found here: https://mast.stsci.edu/zcut/
<span class="gu" title="Generic.Subheading">## Goals:</span>
In this tutorial, you'll learn to:
<span class="k" title="Keyword">1.</span> Use RA/Dec coordinates to search for surveys containing your target
<span class="k" title="Keyword">2.</span> Create a cutout from deep field surveys
<span class="k" title="Keyword">3.</span> Download, process, and display:
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>image file cutouts
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`.fits`</span> cutouts
Let's get started!
<span class="gu" title="Generic.Subheading">## Table of Contents</span>
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Import Statements</span>](<span class="na" title="Name.Attribute">#Import-Statements</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Image File Cutouts</span>](<span class="na" title="Name.Attribute">#Image-File-Cutouts</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Set up coordinates</span>](<span class="na" title="Name.Attribute">#Set-up-coordinates</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Query for Available Surveys</span>](<span class="na" title="Name.Attribute">#Query-for-Available-Surveys</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Getting Cutouts</span>](<span class="na" title="Name.Attribute">#Getting-Cutouts</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">FITS cutouts</span>](<span class="na" title="Name.Attribute">#Getting-FITS-cutouts</span>)
</code></pre>
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<h1>
<a href="#beginner-zcut-and-astroquery-tutorial">
Beginner: Zcut and Astroquery Tutorial
</a>
</h1>
<p>
This notebook is a beginner tutorial on the Zcut feature of the MAST astroquery interface. Zcut allows you to request cutouts &mdash; either as
<code>
.fits
</code>
or image files &mdash; from various deep field surveys. The list of supported deep field surveys can be found here:
<a href="https://mast.stsci.edu/zcut/">
https://mast.stsci.edu/zcut/
</a>
</p>
<h2>
<a href="#goals">
Goals:
</a>
</h2>
<p>
In this tutorial, you'll learn to:
</p>
<ol>
<li>
Use RA/Dec coordinates to search for surveys containing your target
</li>
<li>
Create a cutout from deep field surveys
</li>
<li>
Download, process, and display:
<ul>
<li>
image file cutouts
</li>
<li>
<code>
.fits
</code>
cutouts
</li>
</ul>
</li>
</ol>
<p>
Let's get started!
</p>
<h2>
<a href="#table-of-contents">
Table of Contents
</a>
</h2>
<ul>
<li>
<a href="#Import-Statements">
Import Statements
</a>
</li>
<li>
<a href="#Image-File-Cutouts">
Image File Cutouts
</a>
<ul>
<li>
<a href="#Set-up-coordinates">
Set up coordinates
</a>
</li>
<li>
<a href="#Query-for-Available-Surveys">
Query for Available Surveys
</a>
</li>
<li>
<a href="#Getting-Cutouts">
Getting Cutouts
</a>
</li>
</ul>
</li>
<li>
<a href="#Getting-FITS-cutouts">
FITS cutouts
</a>
</li>
</ul>
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<textarea aria-labelledby="cell-2-source-label nb-source-label" class="nb-source" form="cell-2" id="cell-2-source" name="source" readonly rows="8">## Import Statements
There are some modules we need to complete this tutorial, so we start with a few import statements:
- `astroquery.mast` to query the catalogs and to access Zcut
- `astropy` for handling coordinates and FITS files
- `PIL` for colorizing images
- `matplotlib` to create our plots</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Import Statements</span>
There are some modules we need to complete this tutorial, so we start with a few import statements:
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`astroquery.mast`</span> to query the catalogs and to access Zcut
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`astropy`</span> for handling coordinates and FITS files
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`PIL`</span> for colorizing images
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">-</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`matplotlib`</span> to create our plots
</code></pre>
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<h2>
<a href="#import-statements">
Import Statements
</a>
</h2>
<p>
There are some modules we need to complete this tutorial, so we start with a few import statements:
</p>
<ul>
<li>
<code>
astroquery.mast
</code>
to query the catalogs and to access Zcut
</li>
<li>
<code>
astropy
</code>
for handling coordinates and FITS files
</li>
<li>
<code>
PIL
</code>
for colorizing images
</li>
<li>
<code>
matplotlib
</code>
to create our plots
</li>
</ul>
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<textarea aria-labelledby="cell-3-source-label nb-source-label" class="nb-source" form="cell-3" id="cell-3-source" name="source" readonly rows="16"># Catalog queries and Zcut
from astroquery.mast import Catalogs, Zcut
# Handling Coordinates/FITS Files
from astropy.coordinates import SkyCoord
from astropy.io import fits
from astropy.wcs import WCS
import astropy.units as u
# To display images
from PIL import Image
# For matplotlib plotting
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="c1" title="Comment.Single"># Catalog queries and Zcut</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astroquery.mast</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">Catalogs</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">Zcut</span>
<span class="c1" title="Comment.Single"># Handling Coordinates/FITS Files</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astropy.coordinates</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">SkyCoord</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astropy.io</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">fits</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astropy.wcs</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">WCS</span>
<span class="kn" title="Keyword.Namespace">import</span> <span class="nn" title="Name.Namespace">astropy.units</span> <span class="k" title="Keyword">as</span> <span class="nn" title="Name.Namespace">u</span>
<span class="c1" title="Comment.Single"># To display images</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">PIL</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">Image</span>
<span class="c1" title="Comment.Single"># For matplotlib plotting</span>
<span class="kn" title="Keyword.Namespace">import</span> <span class="nn" title="Name.Namespace">matplotlib</span>
<span class="kn" title="Keyword.Namespace">import</span> <span class="nn" title="Name.Namespace">matplotlib.pyplot</span> <span class="k" title="Keyword">as</span> <span class="nn" title="Name.Namespace">plt</span>
<span class="o" title="Operator">%</span><span class="n" title="Name">matplotlib</span> <span class="n" title="Name">inline</span>
</code></pre>
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<textarea aria-labelledby="cell-4-source-label nb-source-label" class="nb-source" form="cell-4" id="cell-4-source" name="source" readonly rows="4">## Image File Cutouts
### Set up coordinates
To begin, we we create a SkyCoord object from our known RA and Dec. This is an unambigious, machine-friendly way of storing this information for later use.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Image File Cutouts</span>
<span class="gu" title="Generic.Subheading">### Set up coordinates</span>
To begin, we we create a SkyCoord object from our known RA and Dec. This is an unambigious, machine-friendly way of storing this information for later use.
</code></pre>
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<h2>
<a href="#image-file-cutouts">
Image File Cutouts
</a>
</h2>
<h3>
<a href="#set-up-coordinates">
Set up coordinates
</a>
</h3>
<p>
To begin, we we create a SkyCoord object from our known RA and Dec. This is an unambigious, machine-friendly way of storing this information for later use.
</p>
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<textarea aria-labelledby="cell-5-source-label nb-source-label" class="nb-source" form="cell-5" id="cell-5-source" name="source" readonly rows="2">coord = SkyCoord(189.49206, 62.20615, unit = "deg")
print(coord)</textarea>
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<pre><span></span><code><span class="n" title="Name">coord</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">SkyCoord</span><span class="p" title="Punctuation">(</span><span class="mf" title="Literal.Number.Float">189.49206</span><span class="p" title="Punctuation">,</span> <span class="mf" title="Literal.Number.Float">62.20615</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">unit</span> <span class="o" title="Operator">=</span> <span class="s2" title="Literal.String.Double">"deg"</span><span class="p" title="Punctuation">)</span>
<span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">coord</span><span class="p" title="Punctuation">)</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:06:30.721863Z', 'iopub.execute_input': '2024-02-02T04:06:30.722146Z', 'iopub.status.idle': '2024-02-02T04:06:30.726534Z', 'shell.execute_reply': '2024-02-02T04:06:30.726192Z'}}
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<code>&lt;SkyCoord (ICRS): (ra, dec) in deg
(189.49206, 62.20615)&gt;
</code>
</pre>
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<textarea aria-labelledby="cell-6-source-label nb-source-label" class="nb-source" form="cell-6" id="cell-6-source" name="source" readonly rows="3">### Query for Available Surveys
Here we use `get_surveys()` to find the surveys available for our chosen position in the sky. This isn't necessary to get an image file; however, it can be nice to know which surveys we can download from.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### Query for Available Surveys</span>
Here we use <span class="sb" title="Literal.String.Backtick">`get_surveys()`</span> to find the surveys available for our chosen position in the sky. This isn't necessary to get an image file; however, it can be nice to know which surveys we can download from.
</code></pre>
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{}
</code></pre>
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<h3>
<a href="#query-for-available-surveys">
Query for Available Surveys
</a>
</h3>
<p>
Here we use
<code>
get_surveys()
</code>
to find the surveys available for our chosen position in the sky. This isn't necessary to get an image file; however, it can be nice to know which surveys we can download from.
</p>
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<textarea aria-labelledby="cell-7-source-label nb-source-label" class="nb-source" form="cell-7" id="cell-7-source" name="source" readonly rows="2">survey_list = Zcut.get_surveys(coordinates=coord)
print(survey_list)</textarea>
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<pre><span></span><code><span class="n" title="Name">survey_list</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Zcut</span><span class="o" title="Operator">.</span><span class="n" title="Name">get_surveys</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">coordinates</span><span class="o" title="Operator">=</span><span class="n" title="Name">coord</span><span class="p" title="Punctuation">)</span>
<span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">survey_list</span><span class="p" title="Punctuation">)</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:06:30.749504Z', 'iopub.execute_input': '2024-02-02T04:06:30.749760Z', 'shell.execute_reply': '2024-02-02T04:06:32.557057Z', 'iopub.status.idle': '2024-02-02T04:06:32.558187Z'}}
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<code>['candels_gn_60mas', 'candels_gn_30mas', 'goods_north', '3dhst_goods-n']
</code>
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<textarea aria-labelledby="cell-8-source-label nb-source-label" class="nb-source" form="cell-8" id="cell-8-source" name="source" readonly rows="3">### Downloading Image Cutouts
Next, we use `download_cutouts()` to... well, download the cutouts! For this example, we're interested in downloading an image file. The image file options are `.jpg` or `.png`, so let's use `.jpg`. We can also specify the size of the images (in pixels) and our desired survey.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### Downloading Image Cutouts</span>
Next, we use <span class="sb" title="Literal.String.Backtick">`download_cutouts()`</span> to... well, download the cutouts! For this example, we're interested in downloading an image file. The image file options are <span class="sb" title="Literal.String.Backtick">`.jpg`</span> or <span class="sb" title="Literal.String.Backtick">`.png`</span>, so let's use <span class="sb" title="Literal.String.Backtick">`.jpg`</span>. We can also specify the size of the images (in pixels) and our desired survey.
</code></pre>
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<h3>
<a href="#downloading-image-cutouts">
Downloading Image Cutouts
</a>
</h3>
<p>
Next, we use
<code>
download_cutouts()
</code>
to... well, download the cutouts! For this example, we're interested in downloading an image file. The image file options are
<code>
.jpg
</code>
or
<code>
.png
</code>
, so let's use
<code>
.jpg
</code>
. We can also specify the size of the images (in pixels) and our desired survey.
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<textarea aria-labelledby="cell-9-source-label nb-source-label" class="nb-source" form="cell-9" id="cell-9-source" name="source" readonly rows="3">cutoutList = Zcut.download_cutouts(coordinates=coord, size=[500,300],
cutout_format="jpg", survey="3dhst_goods-n")
print(cutoutList)</textarea>
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<pre><span></span><code><span class="n" title="Name">cutoutList</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Zcut</span><span class="o" title="Operator">.</span><span class="n" title="Name">download_cutouts</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">coordinates</span><span class="o" title="Operator">=</span><span class="n" title="Name">coord</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">size</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">500</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">300</span><span class="p" title="Punctuation">],</span>
<span class="n" title="Name">cutout_format</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"jpg"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">survey</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"3dhst_goods-n"</span><span class="p" title="Punctuation">)</span>
<span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cutoutList</span><span class="p" title="Punctuation">)</span>
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<code>Downloading URL https://mast.stsci.edu/zcut/api/v0.1/astrocut?ra=189.49206&amp;dec=62.20615&amp;y=500&amp;x=300&amp;units=px&amp;survey=3dhst_goods-n&amp;format=jpg to ./zcut_20240201200632.zip ...</code>
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<code> [Done]
</code>
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<pre class="stdout">
<code>Inflating...
Local Path
--------------------------------------------------------------------------------------------------------
./hlsp_3dhst_spitzer_irac_goods-n_irac1_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
./hlsp_3dhst_spitzer_irac_goods-n-s2_irac3_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
./hlsp_3dhst_spitzer_irac_goods-n-s1_irac4_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
./hlsp_3dhst_spitzer_irac_goods-n_irac2_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
./hlsp_3dhst_mayall_mosaic_goods-n_u_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
./hlsp_3dhst_subaru_suprimecam_goods-n_rc_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
./hlsp_3dhst_subaru_suprimecam_goods-n_v_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
./hlsp_3dhst_subaru_suprimecam_goods-n_ic_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
./hlsp_3dhst_subaru_suprimecam_goods-n_zp_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
./hlsp_3dhst_subaru_suprimecam_goods-n_b_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
./hlsp_3dhst_subaru_moircs_goods-n_j_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
./hlsp_3dhst_subaru_moircs_goods-n_h_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg
</code>
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<textarea aria-labelledby="cell-10-source-label nb-source-label" class="nb-source" form="cell-10" id="cell-10-source" name="source" readonly rows="3">This list of filenames looks overwhelming at first glance, but they follow a pattern: HLSP, telescope name, filter, then v4.0, the coordinates, and resolution. This is helpful, as we can create a function to match a filter to filename.
For the example below, we'll use Subaru's suprime-cam, which has five [available filters](https://subarutelescope.org/Observing/Instruments/SCam/sensitivity.html) for this observation. We'll select the ones that correspond (roughly) to red, green, and blue light. After we look at the individual cutouts, we can process them into a true-color image.</textarea>
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<pre><span></span><code>This list of filenames looks overwhelming at first glance, but they follow a pattern: HLSP, telescope name, filter, then v4.0, the coordinates, and resolution. This is helpful, as we can create a function to match a filter to filename.
For the example below, we'll use Subaru's suprime-cam, which has five [<span class="nt" title="Name.Tag">available filters</span>](<span class="na" title="Name.Attribute">https://subarutelescope.org/Observing/Instruments/SCam/sensitivity.html</span>) for this observation. We'll select the ones that correspond (roughly) to red, green, and blue light. After we look at the individual cutouts, we can process them into a true-color image.
</code></pre>
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<p>
This list of filenames looks overwhelming at first glance, but they follow a pattern: HLSP, telescope name, filter, then v4.0, the coordinates, and resolution. This is helpful, as we can create a function to match a filter to filename.
</p>
<p>
For the example below, we'll use Subaru's suprime-cam, which has five
<a href="https://subarutelescope.org/Observing/Instruments/SCam/sensitivity.html">
available filters
</a>
for this observation. We'll select the ones that correspond (roughly) to red, green, and blue light. After we look at the individual cutouts, we can process them into a true-color image.
</p>
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<textarea aria-labelledby="cell-11-source-label nb-source-label" class="nb-source" form="cell-11" id="cell-11-source" name="source" readonly rows="18"># Let's create a function that returns the filename based on the suprime-cam filter
def imgname(filt):
name = ("./hlsp_3dhst_subaru_suprimecam_goods-n_"
+ filt
+ "_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg")
return name
# For further convenience, assign colors to the filenames
red = imgname('rc')
green = imgname('v')
blue = imgname('b')
# Create three plots and fill them with the cutouts
fig, ax = plt.subplots(1,3, figsize=(15,10))
for i,file in enumerate([red, green, blue]):
image = Image.open(file)
ax[i].imshow(image,cmap="gray")
ax[i].axis('off')</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Let's create a function that returns the filename based on the suprime-cam filter</span>
<span class="k" title="Keyword">def</span> <span class="nf" title="Name.Function">imgname</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">filt</span><span class="p" title="Punctuation">):</span>
<span class="n" title="Name">name</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"./hlsp_3dhst_subaru_suprimecam_goods-n_"</span>
<span class="o" title="Operator">+</span> <span class="n" title="Name">filt</span>
<span class="o" title="Operator">+</span> <span class="s2" title="Literal.String.Double">"_v4.0_sc_189.492060_62.206150_300.0pix-x-500.0pix_astrocut.jpg"</span><span class="p" title="Punctuation">)</span>
<span class="k" title="Keyword">return</span> <span class="n" title="Name">name</span>
<span class="c1" title="Comment.Single"># For further convenience, assign colors to the filenames</span>
<span class="n" title="Name">red</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">imgname</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'rc'</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">green</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">imgname</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'v'</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">blue</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">imgname</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'b'</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Create three plots and fill them with the cutouts</span>
<span class="n" title="Name">fig</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">ax</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">subplots</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">3</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">figsize</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">10</span><span class="p" title="Punctuation">))</span>
<span class="k" title="Keyword">for</span> <span class="n" title="Name">i</span><span class="p" title="Punctuation">,</span><span class="n" title="Name">file</span> <span class="ow" title="Operator.Word">in</span> <span class="nb" title="Name.Builtin">enumerate</span><span class="p" title="Punctuation">([</span><span class="n" title="Name">red</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">green</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">blue</span><span class="p" title="Punctuation">]):</span>
<span class="n" title="Name">image</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Image</span><span class="o" title="Operator">.</span><span class="n" title="Name">open</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">file</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ax</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">i</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">imshow</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">image</span><span class="p" title="Punctuation">,</span><span class="n" title="Name">cmap</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"gray"</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ax</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">i</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">axis</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'off'</span><span class="p" title="Punctuation">)</span>
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</span>
<img 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q5ORxKutG3mr9KvWyHD7o2bXy5eSI+mzV7COkN69GG3nDQ5VhxVlWOM/KjDShqm1ZWBJDcZ/U4RdkjpOoXh0rKsbnJdwSqJxjwnoDg4+NzoVo11dW61LM3vSKW6pt96rVthcvqeel31Ykc2jfHfinc53ypLEUvORtBJ4zUXDFQd3NmkDg7/bY/6vXvXxApJxN/sq2RnFCtpDg7+NMzG+PGD1qDvr2NQO9MnybY40olptFyXpru/jhxwbUgyd3GGtlv1/2yxRx+f07R88XjZnrJztGupLi7cu/fb1jWtV6+RdHLkUxoDxCxQdzbsXlgti23ryDYtW+0wfQJNo7LM4mkn24xEoj9QBE/nC6R49VbuoMs7I+GcTzaTxfl6K3E7x52e62L/Ls+jcOkdT4XklO/ubureq7FTzgocI8l/bnCkkd5Upic0DdNxVYGPN+iE52Bxabp2Jtaa7ekwY/vd8XvCKcIxjvN1cPAZQZvidursvOOJv9PqlNan11aIrtLjWtfK43Put9oWrhAnJ6XgXgquL5Ctup6fn8fLy6+Xyzriqc7P3oW4a2c6YsmVe2sa4tiFg4ODMXLcofovvcNPjxN5pOWv7HiibMmfnb0se6YXSTK5b401OiKIv2nPqu11Xkmnsj9aviPH1L6QnNOYrdLwEfVKp+129iqRFytgHML47T3sTrovjkCqY/oX2g7GpQqSVJ0ceq4jXtI1yrnSflffih+XkEidFH+msleJJ85bF/Ovts1xGBybjyBJO1winhKxlM53jdl9lOGWZxxnCrvgHl24+qgd07lH7dyKdjfwaFQ4MalskywKN/A0v5NF068o7VXle4/B/7uIsIODg+tIwYDq5BX9sEIquTqdvlx93KCTzTnXiWxSWXgulcvFjtrVxJXop6enV/985F4kq+lXiJ1VOzOzIfdM49IeHHx2OB1FnDG/hq4vHemS4hQG72OsL2C4eGYm12wX0izeGuPtP3tX2VpPOmb5jKt0MV13MylR8e3bt58LILQxX758ebXzt0il+s04k32S2qY2cCc+SHa3O0/y6V5z0t0rh4500r4keaJ1dH3l5sKKf5B8Bf5e5Q86HyPhFj9hVa4qk6TpSpzs0q3mm5XJdL/LViwTTyvMP7FDIMzSruyK6lDKaYz5y17dTSYZ5YKBlEbTOUVZ9a1sa2WbmHbGDs8G8CoB9VGIHUe4fRTZDg4O9nBvu1LYeYdTt4Cy6vC5czNdX8fpXRx1jbbY2Yz06MPT09MbEo/kllvp7MidHTuTSCOW1enyHVuk145NODg4KMxIPEc+Vb7umC9rJgHl8rryV0mGLm+6PrMhjIU6GdJrQ/T88/Pzm/c81TXN62zRKnlDOUmgpN07LCONC0fY8LwSbI8E63DHnS9SZXRzgASau65lPorQSPKtjM0VpH6cjROmYTn6cfI6MsiN8yt6oPJd1TFXeZZVXNrxdK/gwAUD3Gq5+9hegt7k7llnracbAInE2knTkU+aTmVxBoHKwcnt8u+0/eDg4OA90NmUK3rpCum0a2vobCes2CFnAzQP7UL3bkG1MR3ZVdfoZLIfOmdvxc68h1N+cHBwMMOKjp+RTK4cRziN4W3LLfGTQ9pR0gW7KhuhO4/cLozKq4vpukOY9ok7k/T8t2/fXsniHjW/Yjv0PqyQCfrb2a+dcfO7bV1HQrEtrm2/i5jo6nZ4RD+vkJQpHwmkRDqtxOMr6VbwkXyvbeKpm8DpRiSl5gY6V3Sv3PhCN1mSTLOXgnMQOCXJQeYCDEc+1Xkth4y/rsJwdxXbWyvblKcLHjrGVdMQXXmPQJIpsdcd7iXj7H0ot+IecqZ7x+sfRUHdE39im/40zHT6jsOTHqHjgkB6yXjniPEcX7bdrZzRfjib495ttdNmJa6qDvfSVbUxT09Pb/5ZSMuqtO7RirquAQVtiabjv6pcQQqiWCYXaO4BrS+99P2z6ZtH637nPP+OgGYV91xJX8Fq2z/buPpsuEJSjPFrUUC/6zzzzOpgHFDfqjtVv2p6txAxi9eSPIxFtF5HRulu2vpWGb5//27ju5eXl3aXbtmdepycsug7nhjnVD18mbzaOj2vfZne50u7k+6VIyB4jraiI7vSS6f5ne5jN6Y7WRl7siyX1tXv2scx7OxQsu86Dlx5Lk8du/G/awN1nnM+ahp3j6/YB/Xf6EdVPXz5vs5T5RJUTh3naazNdMSO7b70r3a7BFMq5x7prmLWhhUSYZVcIkHF6y5YUHnS43c6iBJJxvL0Gh33biK4Scw8nXLZxc79/5PIknuPe+fs/y1YcRxTv1wZy7N79zfeg3sh7epZSX8lnY6PmdOu6ZxDkLBCTqw+Hk5ZOwKKjoKm7f5BzzmM7lxql3OA9PwVdLbNyXSrjdpxsh7hw9yrzI+gi67o1Y8gN3H1nqzolXuVe5Cx8ujyShkuKN/Z5XRPaP0r77ZN8nWxSEcupJ229VJxZ5/qWF9AzrZ0AXvny7mg3OUj8bRiXzR9XScBo/ZnV/6ElfRX4ij97cgoln8vnXzVH9glcRwxeRUkOrs5kWSYpe8IsoRZ7OPi9tX+SOTaDi7veLoHPrKBTI9BuJuWBo+bSGmF25FTNB736K9OESZnneBAnRFWK5PkIzqUB38v7mGUDj42OnJK7/+KbrqyerW6aJHSdGUm56J79O7p6enVv+Cx3BXHtNJ3RFlHDK205Sru7SQf/XBwcNDZEfdOvlV0Af8udn17/b3i3ztdyJ2wtaDR/VFI7Wx6fn4e3759e0VC6YLIqj3kYocjnygLiaQZZvdpFh+6dG5hq0N3H7o8M1lm9a4s3M7yrxCZeo6yXSF87gmST/Xd3cMV8tHNRWKF/NmJ5Tv5kt92BZd2PBE778V4j3I0za0BwRj9e5w6WdJ5rt4znXvHx1XyKfWBDppu8Dnm35FXep71doPepTkO/cFHwCFDPz+cbu3SVKBwxXZU+iuP3DpbQ33sVuYJ6vLOlnz58uXVKnIFBXwcQh/3dvVzESM5JO6c5ku2p3NwVu7RvUis3WDgYB9/e5+ujueDx2GF1CC6e1J6tQsk32vxtZNzZVztxiHuPba6uOHe51t56oXkY4xXpNPK7l/9rXavynK6nOlv8QHqWO1hWgzawUqstqpDEvF0K5m0UrfCkVyJSNM+dYTPGPPd7qtx5s69d+TXTlkdcZZib4cVP4nEarfwx/ruteB26eXiBT6re1Ug98zvowZ5h+Sczggopiuk90WtkFu6KuD+CcKVV8o5DaLU5qRoXb6V4GCFTHoPwuk9HbS/3WE+OHgv7D7SvbtydAVX5v+MyOhkennp/6VVHTf3HkL3Po1y+HlNgwf9KGmlur9biSTSu7Fmjo5L5/rtLGwcHBysgiTFTp5b0BESKcC+il1dSH2aHtmu6xqPOLJJFz5WyB2+5zA9tqf1OfvAdxxqnyZ/YUZCOdvV3S9nmxJuJQlnZVMWtsXF9LvjZSef66tEiDnyaVbfij+yi9ncfO/Ydqef3b3t+vue5NMW8URlfEvlqy8Qf09QYXEgzd4zogqQ6YkZ0aQyqKJ+efEv4VMZqHxXSKeEFcPQKYyUfyXNwcHBwb1w9ZGHGai3d0BjzpeZpseyVW+nBQ8XMLBMBhG62u+OuxfXOruTHBnXD6t2Ztd5f5SdOTbr4J5YGU8fxVf+E+EIh505PlsoYNqVoNSRJLtIu5R29WGyOc5GkXwaY/wkn9xCiJartmqM14skX758Gc/Pz69IJvcHTAqNx/Rl5bOFH9d+2jiiI0O0f7o6ucCzcu95P1bvK8tOi0DdQh5l70jUTgYed2lW+kTlTiTKjmy76XbqWdUdWqYjBlf6r/Im0pT5nN95K6G2TDwlomi34nuVw7IcrgYCs05NTryWwbII9xJXJZqq7CrLpScB5VYa6HTr1tVOzo7hdysc+pv50+RIae7l0L+3k3YCkc+HKw7mwe/F1VU3PT8rY2c8XLEzRJJHFyRcfU7/uvcI6jgvcqsCAdoGBiruuAKIznlJDmI5/jN0q2zOBrGOFVu0iqMfDg7+fFzRD9RLTt+Q7E+6qYs57gklUVbqcOkLtXOJYLxBAol2Rwml9JRH2SZN9/LyMr5///7K1qR/vUtB+wpJkGyZqyf1oX6zXF38UfkS+ZTs4Q6p0pFkSX7K3aWb5XHt4X1xpBPPuzl4Kx5t83dl1DGgxKReT/PQnUvkU5cvlbOLy+94Sgpot6z3JgdWkIiYMd46w67N6RE7hxQkaAChZaRzVW9nuLRs9+3Su3MzQknbMhu8K2kODg4O7oWZTtadPfcgla4gES5dOv3d/TlGQXdX6aqx7q6tPDzmIgedIvdIg5PTgWl2HKqEe9qZY68ODv4OPCI+oV7Wd9u6oLv0jS4M7GL273YuEK00K/o49VPJWqRT5a1FD42hSDp9//7950vGy57Uo3WJUHKEVMlH+VeIpi4YT3bAne8WTfRbF3XSoujVYN/J6QgtypjyznCljDT+OQdWCLiPjJnMel8SOefmYBqvs7Ha3U/OryTzDi694+mWSj/jIFGklVTF6ssJuyAhXVPWX9Mlg+GUHOWeGRx3LrX9kE0HnxFnTP7ZeNRjdo9G0u8uHdPM7BCdmHLS0z/faRrNxzpmTuy97cMttujWOg4ODv4sOJ95N5/m7xayx7iNUNrBlZemj+HjhQJjjBTTqa1Qoskt0qvNqX+2q7RuVxT/9a7KqbrUZqV2pOsuMO+CeEckdXBkmH5mcl+Npymrlu9Ir66vdurt7KjzE9yny6v9duti1b0wu19pg4fu7HP5+TvpmoS0KJjS3pOvuUQ8JSESk+/w0Ukn3sBuwCfyycERTLpCPMZ4w9JXHUo2ab181I7viHLyuhUAlU8HcUdUpfJ0dcP1H+vuJtfvUBa7KwBM81GClFVGu3B1Xn6U9ipWDdzBnwc6RbvO/cxBf++xtap/2G4+qte1y73TieRTlUe9zlXJFWfRyV1l0UFzbVjVaZ8Jn1XuFdBPSIHde8lycEBcHRergS/rUbKFRAx1akHjA1dPF4d176nlXHx5+fVYtp5XzOxSIpe6RXdFvUBcFz60j+vPMPjYOGVjnOX6mbHIit3SdCSL2A9dOfp4nSMXUhxFWXg8S++ur84BF18mO74bPyXM+jGVVbLu/JHZDonj+lPL5WsJWLeOXZW1xqqOyRTXz+Tt6t8hrTpSdBU3/avdDnYG4EfBaufeq00MFriryf3TXQ1QviBW07CONCnZFpJPel7zrRgflyaVn9Jcwc69ucUZvWJwdsu6pcxbZLhnfR+hbQmfSTf9DUh/ZrFyn1Q/XrmvTh+u5rtS385qdxc8zNrc/UOrc+4ryEnvEyzovxaN8Xa1ko9EXMWKHV4lBd9b13xk3ffe6PyKlbx/Iv7Udn1UrPzB0T19+535rXrTnb8aT83auSNjJ4PaT9orrat2JbmnOiq92h73nqjZjqeSJb1k3S2SkCRnm+qbwXynzxJxdy+76MiEVbg+SURYpb8l9pvJcoXkXCk3nXNx7yputVtdP4/xlpx1pFM3XmfyzOJ8lVHzVZ53IZ5Wb8hH/Ne6zwYSSI6EUbKJSt2d6wb4jADS+jR9pyRWiKMVo7yaRmU9ODj4nLj1H1Rd+ns9ytCtEiYSfwb+Y1wHrWP27g79rfqdL4RVp53OfcmnhBMdfO6scgsnSS+rXDx2bXbn0vHBwcFBh0fEJytlXlmsTHbN7RrqYjCSOysyJ7syIybSbg23u6POl01Re1PtVEKpSCe3K6rkqLZqPv4D+Yr8OzEI0ZUx8xe6Op29JBnQxWcd0fC7sEuYdTHtSl1uXK/6cPfyM2axub7XLOW91+aM9/CdLr9cXJEcZs3z2cmne8m/Ely4rbLq6FOW9K9DJJ+ofK8MMMeKur6hop0FEx2BRSXQBSBdWQcHBx8bHeG0o4Pf096sECr3BhcYFCSxXGCRApFy5ukE8YWuqmOfn59/BgRVFl8Cq+U/PeX3Qs1sCdvujlP+g4M/ASvj+bP72++FRKbcWs4sTWczdsopPe/iipm+10WIMV7HDiVL1666po/jaTtoJ+q8xiV8bFqvJyKEdoXvgSoiqr6VbOKTIVUOP9ofKvvqeKl2a7v0exVOLhcPJbt564L/R0AX9yWstmtlfK8g+S8dqcP7t6t7Uoy9i87n2pFjBzc9avfIraqf9WWwM6z2B42GMv9jjFeMva42qzKptFzBdkZBFWUyGizf/b5KTLm+SaSUM+BdmoODj4AzJudIq6eKq+R9Kr+D02c8Vp08I8k7GVZ3Pc3awEDABQaEtkVXkF1QUsSSLnbw77HTH2BwQWRFrmRzkuPNPN29uAfuOa/fa4X1b8fKPTt9/WejCxLTIu9umd2jzURaoOVvJU46n9nV05FRbgGjk1ffS8Q0qoNZjluM0Jij8rtFivqtaXXhQxc9ypZpe1x8pPVrDETyS/uX0BhNCR8XH7m8q0ix1i7xsEKivbe/urLA1M3Re8h7hcSZ2YnO/2BdO/a/84lWwfk7Iys7eXawRTytPP5wgqseK/3Dv7mufFyNGOPtarZ7hpn/StQpSQZRzni4NqmhcOgIrMS8X0nj6jo4OPg8uKcNuecCxsyBVB3Z2cpbVqeIe9tbddh1hZXv0NDrSlRVGW41Tv9Sm/arw46z7vr22IODg4OEewfciaQprP6z3JV/oLuC1F4+Ps08Gkek4FljljFe74Sq34wdmIYEVf12773RxZKKdxz5NMbbXbyUhwvxDh1Zou811BdEp/50ZasNntmxVb8ijfePsnDftWO2s+8e2I0jOb71/A7plMrsZLv1nlFGndOPxDLxlHY3rTT2ivO/w/zdio+2u2r273V1rb51ldkx7OlFfY58cttP63yVV0gDlBNKCaxEJqVgYZd8Ojg4+LxYcTiu2JxbnJNu1TM5pCShHDl+L4fJtdUR/TwuGZ1d4c7a1Xc81XnunFK5NJ17sWzS5c5WuFXfLt1ntxN/Qhs+Cz5iX6/IcxZ/1zBbOCdpsgu3y7b88RmR4c7RZnQ7ZJMuTP62k7NLR+KJZZBY0nI1luDiuqZhPiWeilBSG1Q7n6qs79+/j+fn5/Ht27dXjyTSBq20vyMXUrtV7hVyIo03F48lP2Ilnura4/K+F9xiFbGik6/q7Hu2O5WViCJHJK1iZ2FOr7t5rPNn1dZckfnyO552nf9dFvGj4T0no3tMoc6P4bfA6vPLNRj0HA2e+4eHzti4CZMIJWIlzSo6henkOzj4SDhBwR52/v72PZBIp053ul2kszwz7LwLK10rebjD1v0r6so7nipPvXNDV5vr3/FUDudgrtqS7trvIAxm9/BehMGxaffDPe7ZwZ+DTodeKSP9duPKEf2KtMOBjzUrZgvqV3VS6e6Vx9ep40nIqJx851Pl46N1XLB4eXmxpJO+46mgMZKmr/KSTU6LG65fNL2L41xed6+6BS+34EJZS85Edjj5PyJo199TzlUbcFU2HW/uaSV3/0hC6rUrmBGfq/l3cOkdT7uk08E+SD5xkKXggHlIPhVU2SZ2vNJpeVpOpxBmK9HuWHGvNAcHB58TH9mGdCvWKwHtI1bX6HxqPV0AwXNJp7pdTw61ylyO/xhvH8ejXXEyriwszEi8Yx8ODg528IjA9p6LobQfs1237kXduwG1/nZ2I5FQiWRz/aHl6gJHxSorJEqRTno9EVYrO8+cfensEm1TsnN6XW2qk8ORDFpHksVdnxFgHwFJ7lW/6ZZ23TJP3VzpZHLt2fWFblm8dP4ijx+Fm14uXpj9q91qJzLfTprEIj/6OemrDOdKXsfU1/kqh484aHpd0U4r9W6nVFrJ54Cc3UeX1inilG91leE9FOi9Vqzvgc5IubRXsHOfPzPu2bZHG8W/Deog7o7jRzhYXTkr8rl3QDlyx0FtAfsj2Vn9vfI4XqHOM0CgraGzosRSpX16+rViTVtDud0ChZOZDtKKrprpxp0xkgKe3XKuYoXYXDl/L3t1JYh9ZD/tyLOTZmdsXa3vHjg2Zh+JvC+s6P6d8eR2NnULLa7sRI4wPXdT7QSr9P9Vb5Ns6vrBycZYQ18V4myQptP4hvZTd/COMX4+ardit5Tg0B1YBMvoCCDtK0ckrZBbM6LO2Xo3frX+GdHQtYvpOhIlEWdEGs/6nexsmrdOFie/S+t+d3OHxOOsTvav9qO7X65+50NRj3R2y/VnIjs1/Q4R6HAz8dQFBbvndzFTcGOsrZrfEtTsosubttmO8fZFf0xDZaxBAsuv/Dr4u4CCSAM5KbqUZgU6ER8RTN4TVwOCW+v6SKTYR8Lf2OY/GVfvZzdX3nPOpvKv2J/O0XI2ebUOtSO6cJP+xKI+fLTBBQyVX/8xL6Vz+TQgGKN3EFewY9tS+qtlXcWVOfARbaXDR9PXSZ7P0p8Ha0hBXArIx8gEQIH/+JbqvCU+cYF5l4fvhNqZb450SnZIv9VeOPld7OHS87d78XmVR1vUvVxc30WoMpMAYF+snGNfXN0I0ZXd9TnHcbKbK/IrVgiJlHYVV/NxTCYCpdKmfEmeGYmk49cRfExPOShzIoFmumXmG5F06nSdS8/27GKbeNJKdhn698CsAz8iZjvGxnh74917oNJjdZVGB8wsv2NCnVwqWzJ8s4G9cs9W0ySZd/EZx9FnlPngoMOV3U4dbpkjmvfW3bQaBNyyckR0793YJZ/GeL1zqWStsupYdzOlnVwFdfTd33cnIuoWG7PjYB0denDw9yAR9Aykky/uyiAp4/TWbLdJtwh9C7oXkiestK++UzCa6uS/qM52x2iaZF/GGK/si2uze9xO4x8u5N+KW4L01bILbryu9O1Kue6Y45nX3wOdf5BkIgHkykxtThszZpsyks/y9OQX1BJ51eGWvp+R1/eqb4t4WnFmZ8I+quNWlPlHQ1p96F4iXscr5JPmcf8ilF5iPmN/CzsBk0u7cs9WlFpn2Hdwr3LeG59x7B8cdLj1X+k6x32nrBn5n+pYwS3kk0ufHn0ozByxOpcImlrFq2u0HQy00gtvyxbpvw1pf9IRU9kofxcQunyORNNrs/45OPgMOGN3jpme7H6zf+nLO51YcLsNHGY2RuVIOxxc2cnnd1iJ59xix8wWlYzsi/qUnajdsexz3Ymru5Xqzy00re54Urn5aDjLrl2/lK1+79qJWVy10tfpvnZ2b0aGrIBluXjjSuzhiJtdeV08qMfJ/s/k4PdMrh3SSespudz94vkV0rhbfEu/d0nJrq4VXP5Xu9VrhR0n/Sor3E3M98DVlYQkq9tOqkZDFZk6+vqYnT56Vwo6Pe/M1WhVtimIcIp4BUlxzdIw3UqaXXm6cm/BPcdkJ+fvGPsHB/eAI5ycQzuDkvOFW1ePU7BwSx333vmkdmG2mNA5cs7RVruhcqvd4EvEK79bfU62Ru9d15+zhYKrxNSsf/4UzNr3WcmL975vf/o4+VugOrJ7T84Yb9/NlIim5KvW71lc5fRnQrcg2wW9zOvsRLInqc94bcXW6DuV2E6SbGkn0qzNamuSb6+klL4vytlFyprKq3pp6zqyYma/dgmUq7YtkZpOplW4MXYv4qOTKY1hJ8MK6aTpZvdjlThLJN8qHJmVZNghMu+Jy+942nkE4j3/neieRMEj0PXFbKdTEUh1zMcS3A4mTUPyiXXxmWclpro+pVHo0uww5fdKs4KPPm5W8Ce04eDAGWu+XHsG53zci4RKwUjVcQv5dFUWffwhBQqaTmV3zohu+05/RsEy9PEEXdhQe8Jr7K+yb7W7qo53+kMDC4dZgHZ06MHB3wENOpMuLaTAfZXU7+Kl0leOCKKc5cPPMPPZ+Wh0Stf1Telwyjh7x5WW7xbX3ftoHWFURI7WrbuXnp7+21mrdWnsVHk1jXtdiSOe6sOXnKc4xcmg5ae0s/5zfezIi1vtGskykqKUfwWd7LvpZiSKjp1VYnQVXWw6K9vdm1vuVRqHnc5ibN7JcQ9S6uaXi8+E4BbUFQf7XivTK7ink3nlhrjH7dLzyCzf/aMddzvV4OejdnWejj+fuXaP7RE0BklJdIp0Vck+kohKeT9yMPKRZTs42EVHOl11EqiXSu9dgVuJdXWl30l23d5/FR3x5IKp5BRp/3z79u2VLVBiqcBdTko2ucWMaq8LLtzjeztkXrIzzvns+vHo1NMPB38HnJ4s3aQ6yu2GqPxXbBJ1UiKwusWN0pkz0oPyVp1qd5xdoEzJvnTX+XqPSld1UQbq++fn51fv/9O26GIH3x3IvmEspAQeH8fTekhU8eN2+KZ+V5TsXRrXv10MpUhE0Uo9WgbLIlHStXnWlk72FVk7YoTjWM/NxnEqr4PzOToS0ZGDM6yk6fyfXR+abZ/5u6u4RDytOscu3T2dmVtf8HpveXaR3vHEm+3aSWNJBp8r0+7Fee4aB5gre4XFTegIHS1zlmZFwa/KmcpaVTg7mMlzpa5E+r33uL46Jg4OCOeA7+Z/L+J4p/xbdGeCe9TDXaejteL4Ken09evXV8EKd9zWo3aavsrWaw509J3dIZyeTo4mx4JzqK445lrGZ8MjbNFHwXvaol0f4+DjwREtq0HaLHhNi5l1jXU7UopluZjAkQGdP+t0fmd33S6mRD5pnpRX26xwi90at6hd4KPqY/x6sbjGMHVe+1jtVNknylDHrm79zN5HmJB8d8ZbPOfKYXkqy+zeJpBkcuecnB1m48fVeQ99fsWHXEFHftVxist2SKdVdKSTGwMzInPms16R/eYdT6uYDbaruAf59J5IZJx+j/H2ZqfHRDqCiGnq2DnYHTGl6ahktMyOqEmBYBr490qzgtSej473DK4PDt4DtA2JnN8pKznyuytzRYiMcduu3FvJp5nzyeOV60kefZTBOTQ8p8592vHk6tIdT3SUZgGbC87Sb3d8dOjBwUHnm+txp3sSOmLApZ0Fgx1m9s5du+Vx75W83eLISiCuZISSToxXHMmksunuJL53UHeRpXgoxUaz+3qLvU/+yqxMRxBpvpX83TlHPjnZV4iZW7Ayn1TG+uZnN25drd+VpffmvX2O1N+OF0i+363YJp5W3+10S8CwQibxnR8fgXxKjO8sCJj95qTWVWVNS6WoK8x8cXil0X+ESDurNGhwLD8ndOobF3R0A3uVfJqV08EZOHd+hluV5xXcQ3E9WumdQO7gKmY7eTQdQXI+Od2rTgvL2cVqIOGM/xi3BQasr3P6SKzp+yv4xxYEnX595IG7odx7IV5eXuwj3rqirau3alNUF/I+rQQFK+TTe+iylTpc36/mS4HDLfI8Gr/LFq/We68++gh9/TdBg059zEvPp3zuuKuHv5O+6ebn6kJsJ5MLmJ18zi6sxDWpTi1z1q/6+FyBj2lXOm1vIohINPGxcP3W9+A6mXis+V2/zWI/2jaXLvkE7rfaSKa7ZcGrk28Gjnf200q7U7lafiLKunkz88u6Ptv1H91v9Vdmssz6exZDaxm8HyxjRgbPZJ5hmXjq3rORHOLVDnNwf4G5m16d5Q4rjlxqQxcMuD5KA3HVWBT0PUz1uxRunaOC/f79+3h+fn4zGevFeiWnWwFgQMCVaZ2grt8rTadoCZY5I7WYJim3K4HFPYK+FazOlY/Ckrs0t8p2RcE+qq6D9we35SdypDDToVyhrLQrjtg9x5pzmCkDnaTkuDond9ex7OwTCShNq4sQVS+DhfTOJy1b5VCbQduiCxwdwUQ5XZ4urZarv10aYtbvK/dldazN5OvS74yPR9oYN2d3SbGV8hNuKf932gyd9/dYePqbQVKCdueRC9q7Y4h6bwz/Rw8zokOP3ZMTalsqnqAMla7q6zYZ6LtmnRyJVCFxRJuhsYhLz7r4mLf+A+uXL1/s7ly1gVWeW9Ai4dUF+oy9vn79+up9isQVW0NC0cmQ/KtEXM1kWP1N/cW+Unuvefg4JX+zX/Wats0RL65PUj76HE4fz+B8lC6tflIbu5jW3d+kEzp/a9amHZ32sEftPntA55zaR9RxL6zuEqOh4nkd2I7ZdysJLghIQVGnlAnKs6oMV9LdI83BwcFj8VHtSKdvV3TxqqFOwQQdkFn62TWVyTkmzllNpAEdRhfgMJ2zOxUQMA3LmTmczDNz9N7TzhwcHPydWCG1O/2hAeKsHkdOdeRv6a70pxfORjjbwp1SiZBIur0Lup0tciRVlc9+Su91Ium0gs6er8Y9O7Z8JgvzXylrlYSayaHHHSmk36kvurHEsnjsfBste6UdHXnVIY3dJLfWrR+X93f6GDPCMWGLeEqK5R6CfETMHMfZM+Ep3T36qBt8JIncOeZ321RTGXWcJuSq3LOJ2ylOp1DS73umOTi4BWc89UiPOrjVR0Wne+7xXiath4Q96+9WcVfh9GmRMbymq8or5apcutPJOTa0F09Pr/+xrhxT3YHLdzo58ok7a0t+3RWlK+pu5a0L3NTJTA5+0vVdYHBsyMHBnwHqQIcVna2PZs3qKqzsJOh0XvpeQbdArGmcjXELwS4wdrakI2Yc0i4mvh7k5eX1kxTJHjqbyn/BIxlVZSffoSMEUhtT3OPIPbVhJIFmCyw85+q9R+zJ+54+Tm5HbCWiLtXHPmIdxCoht9o3ydfYye/O6SeN548Ad49nWCaeusF66427Vz6HHWffKZd7EUlJOd3SR5w0nYOvdbmX8dV1dfxV6dcg51ZUVYirKy+OROqMhLvO4KCb9Cmg2E1zcHALPoqh+MigEaPB1X9Km5Uzxus+73bfrMDpmlSmO+9Wm4m0wqzX+fjDFYeEK4jOcVDix9mLIpYYlFR57p2DTr7qGyWiqlw+ouec1s7+dP1z1Ya4NHTsrzigWk/CZ7VH7yn3ii9ycKCLHPWoXZ0vuHF769i6Sjp1mNmAK6QDy+Cj4jM5uKjR+dSzmEkXItgGvYeVho91l32hDdN2qs1yCzyUx8VVTvfovexijkQ8jfH6zze6+KXr05X7PbOnK/fOESZ8xYq2Y7Ue5z8xRtR7382BWR/O4sFEWNH2d+2b3R+ST7vzNV1TXzS1cxW7Mha2H7VzimWW7rNg9XE1xaydj+wjNwnczoDuXJ2v8rjbSa/XOfcvRaVMZvK6F/dVG7p8KxNiNd3BL3zGeXrw50L1kns3xBh785xpb3lvR0cuMF3VNcZr/XqVhJiRJ+lc1a9BQLfK50ASSZ1KraPAd2S4NMyrtsy9a0MfBd8h1rrrqa0r5M890qR8HwkfTZ4/Gffs63Pf5iDxxHNj9AHZ1cVJF/hdSdPVvRpIpvyzHRyJRNF0andW+mon+HVxjJIa7pUglc+dc0+C6Bhwtsj1i7ahPp3NXbm3jnB5zziH8rP/0kIMP9143vVJKJ/KseKbdfXf0reOvEoLbW5czspNZNWKrtCyKINb0Hskbn7HU5qACSs7kO71Qr8rzt9OYLLadiqNLu3qDq1uNV2dd1cXBxtfHE5Hn32igYHmWw1qnEItpIDK/U7GMU1093unnIODg/dBeuSuMCOtO5Jo59G7me5I5a3q4gT3z28r1wqdo5t0aVeWg+ar1V4+oq2r4/w31kqn5fBFrrrDTYOKkqt7BGIW7KTxccWGrJbzmfCZ5d8JXv80/KntujfSjqfOr+98zx3cco+q7it2ZUWG1LY6T72edk1o2vQHHyx/RWbuNkp9QeKJT2qoDFqGXq8/Y3Lvg3Lxj7alriX/xPVtB0fwdIQo61khNlR2zeNIEtfehBVSZ8Vep9hxNmdTW2ZklZsDnbxuTigByRhZ87qdhFqe9vlsLjnZec3103vZ+0uP2l25Psb7/TOYYlUpzzp8d6fTrfJcKZdtoDLuHrtLx0yXAi8q/aRIqTgcAaRYCZTcb03fpenOrSjLPwE7Bu/g4NGYkU7ESgBwa5Awc6ocQT+7toOZY6rvtaCzkspz+VJb3T+dFNLq8hjZRqSAhP8+pOmqHJW/6uwek3ckUn3P7APrSmn03CzNZ8K95D82ZA336u/PPu7eA0U2qa1xAfUYeYGyylnBjo5inZWG6Z1u1LpWA1LVq679riy1ISxH8yTSKQX6+tFzmrcjnyqAL6KJCx5FPul1925B985Ct1O3u6/8JnHJc13fsOwuBlpZOHFItpJtScRTktvdVz2f2kkiZ7eP0rGSQUleVw+PZ33giKREICkxxfGv13ns2pl0VYpt09xexW76u/yr3UclncZYk403aRYkrLCNV+TY6aO0as/BRwXN327gJSXFd30kUqsjh2ZGXeEMbZIzKSLXxhWlfBzlg4P3hyOdOt3Zzf97zuuZrnF6cEbm3AJdcXUOR/qdynKk1Q5oD2gjnM2gLqfdTc47z7k2zvp+hXxiWTtpxjhky8HBR8bXr19b0kmxoktm6B49npXR6fHS3QpH4O/Im/zxLn/3CNot/ebKSCSIkk+az+0SJkmgLy0nGeXKXCH9nO1y5JO2r7PBLh5y5ALrWPEDHBwx4uaKxn6UJZEpHSni7HPy73bi8V3b3Mmpx4ks6mRIPkxHdDmOgPeiI/+Sb9iRZR1mOiHhEvG0SyL9LtJpF1cChK7DrxBJtyAFWiuP3dWxKtnK74irjqV26BREMurO4U9pVibOTKGfQOE2fLT+u6c8K47hwf1BIz0jl1x+zvt7veOpwPoT+XQVKX/nwFJOJapcuenxPe2rTv+OMV7ZhpktSo9rF758+fLqnRpp0YMLHtrm1PeJNJwFlzNnfwVX9MSfrFuu9uMj6v4M+Iwyf1SskE5d8F6/r+h2DSJ3FrmTLXB2Uo9Xd9yukk36GHRK74iv2mXm+m6FlFCkhQr3yJxbBKl66jx3QOl7bNk22j2tP9k0lbOzNeyLhJkd6mI0RzrM6tKPtjnFmFqui/30s0tgdLY4+WbdXHVtXyVjdkinBOeDuHmQyN1Ehul3Rzql+HoHO3m3iSdOspWB0m3F7OpYLZN5kpOe8jplTRlWZOIEW3kXh8NKn3Z/4eoGppJIGoCoAeFkpnPvFGl6HM/J9PLy8qbugut7nXwku64w148GV0RY73sSIR8Fn0XOg48BFwSk4zF6Uqa+Z/pmhpkOd0R8emTM5V3FzIHg+4/0+tPTU3xUoMpMdjo5dqqDtZ9pi2l3NH/ZMe2vSs/+obP/8vLy5p0dJYcGN8neO1KSzpe7Py4AdUhBXJd+1665uj4iObEy11b6sQsyrvbde+FRtvDY2GtwpBN3DynxoPlSoJlsVTf31f+mnUiPbrOeFFSr/+yIrtl7rVLbUlvSNbVRurDt6lN7TZuXCCheI/nk7GHFNvyjJD5C53ZCpd1O2l61fdo2J8sKEUSSZabnWE/ZxQIXbFJ8xbGhu9LrnJJ6tKE6h1iuI59S21fGaMWYrIvoSKgkQ5dmlpbtdL7cCjQf29C9DsHJk+TXecD0XVksp8NdHrX73XDKk9COTuy/YwB3wPrdBH00VuXu0pFIqnOzxyk0TwoQk0LTdJ1MLs2uw7nSR1fu/8HBwW1Ycaiv6mX3jrt7wTmP1EuuHenxi50Fi10nxulelnFl0UTtAeVx7wjUfK4Nrj2so5Ol2tXZGb1vK0HOyvWDg4M/D2ned4QPz7n8CsYyzp9WORwxkMqt9FwUSfmVUKDcdU0J/jHW7UZ6t6CSToTr086uuPawf+qc26VU55+ensa3b9/G09PTq5eMl/z6SF6SnbKwXSltspUpViW5kcZgV/cMs4WX320XOwLr3rLxflyt5+p9uCX/DCv+5FU/+sMST7MGpR1OXT4dILNVhVvwHo8idkrOKQUqJ+0LFyDU+UQsOed/ZixTEODK6Mi/WdDE82y/+72a5uDg4HFQnbSyKnUr7vkYeJK12tHtUi090+3cXekLFwyk60nXaRBxVf91elztSrIVyQ5pP9EOrfoBdZ074FI/aJ5jHz4uVvyLg4N7Ic3/FPQX0s5S548rkcAyOvvYPX2gsidyn+1x50sOylLt00fxHNFF8on2UfsxxQEkXFwfsN6yJ2oD3BMV3HWrtoxtTfVWXewD9gvlX4lp2Bf87fqvG5ersRbHY3efHEn1aJupRKQbd6s2YiXdzmaHLt8O+Zj6+RGk2qPwW4inezNpqwPKKfBu1ZmEzA4oU2cgUp4ZKHsioVSJcYXAOe5U2N2qi1stUGPJ1QjNp8pq5ZGGOsf2ddd2A4UVQirlOzg4uB2OdFp5H0Yqi/qvyuFjFQ4rxtzZD1cuH2m+8s4N1pMe3+sCju6RvNW6Z/KqLVE7xSCFNsm9A0q36nePzSX75OyMCyK0P7Sc3QWLg/eBG5/nPvyH0w9zuKC0zqv+GmPtkTcG49Q1HZwe08CTxEFnD+vaCvm0cj61UXUybfbT0+vdQeybSpt2+CbSxMnqnsTQfBqDqDx6XR8DL9lVprJDz8/Pbx5RcwsYqR+1zdoXKveMgGI6LprwVSVKqrm+3BmbqYxEPqU8jySgZiSYi0UdVu268xFYZycrj1fqdPlW8zLdrn64Bz7sjqcZnJO5m69+82ZRqa8EKFdkm72XaqeejoSiokq7mLRMvoyvylRFz2ejtd5UTpoYfE9H1ydJgeg1l077wuVLaVYch4ODg48PBhSr6Xfg9LAr516P/nX6LBElK/Xu6D2tJwU8iWRiHpJP+l1BgS6auPduJLm7dz+x7a5t/L1qH96TCFi5Z5+VmDh2+OARUJ2gvqjDChmt5SboqzgccaPlV1lcpF157FjL2CXJE6GvSOWpbkwEGOOGRC51Pv8Y/pE76mVdBHGL72V7CmVnqg8Yy9CX6GIqLnjVfUz3I8UmXbr6ne519w6gdG8cKebkSWSPq+/RtieRW2kcO/u9QrYxvfYP83fzJo3pHTt+S5+y/TNC7h54d+JppTG3pkmrwLP8HAD6vPWtxNCKjLtlz4wfFWKn4Ei2ucCgnH6ST6pE3bPSbkeUU3xOQc3Ip5RuNXBYSbNrrA8ODq7DGeLdnU6KFeeuy3tLfe7PFEjS6LUVqL7j7qUZ+cRgoGTodNwV8snlpVPekU8OXNXWPG6BY2ZnnC1KOn8n2PwM+OzyO6ys4v4N+NPu66MwW+BUzAgeBqg878qr60pAMc0KecBYQmVxpMiM1JgF65RVbVAKnld0p9PHOzY6LZZr/EFSkWST5ufj3c5eqe1NT4rUMUkulZNjcSWmmfWH2rgUd7m8XdnJrnbE0++wNU6WVf9mhXi51Z9cuQ8rC1pXsULIud9dOTv4kI/aXens2e6h3Xc6JRbwVjjCifWsMp00IM7gVJ1qeKgk3WCr8mi40nPTK4/nuZWkGeE0M+SOZXb5O4PmlD5//82O7MH9cMZRD0eQ3ANubq86CqsyzIxweoGp6uPZo9NaVycXnWzqMkdezYijVdBOJEduRkoxryOZ2L6urrQCukowXQmKCu/teD/KYfwISIHPwcEVUJeq3uWLuBWz4H+FrHKkE8+lOKRsyUqw7/zmjuRnWd3vlTxVZ5JP86h9cuRgdz9od1z8oXGNW4BQokbTMb7RstwTJpVujPzv3+7edjHNjo3WMlb8mNV0iays8909o/y/g5DaGb8dN3B1oY79lPKt+MCJPL+lX1fmWkq/ik/7qN0Ymemf3Sj3Tg7N955Ew1Vya5UkUQXIFffuHJU/V6n10YYUNKwEH844OqXrzmsZ7loyXon8Sr8PDg7eF1cD/Bl2negd4mkViTjRR7rVTl3d7ZXeAdWlvUo0jfFW386CgAqakt2gndH6HEHlVpgLK3bGtUHT8fjg9+Hch4N7ovP5U7C/sush6XAN8pXg4LWu/HqH0so86IjatBCvpMUOVI/Xb7fbtys3XUsLBzOyR/MqgbMqg9oXJZ2czXKE3mw3L+tLMQ3TObix6sYUSamUZqWORKakfJ9Bdyeij2lWsNKPyUdx9RXRl4ixj45t4mnHmX0vrJA3icyYlfdeN/FqcJPImXSdcORQYvA1WEjs7Ar5tNLeFcKIaTtngAqPZa+MC2J15eEj4VHj+Z7l7oyNgz8Lqzr6vbDqeLvVyQ6O9KdDfK/2rzj6Y/SPTGg5CY6g6VaDFWl3gHP+9WXjXTld8NI52O4eXCGfZvZqJ8+98Tt16HvU+6iV2lvwEfTZwVusEhE75emx6kFHMJHw74inMV7vwnp58X/M08mk+ZyOTnkUbmduIspWiP1VuVMdKVagbdWPk8G9/8m1s+5bvV+wk6l+u35O9n6X/Fkdt53eT2RHJ0Ndq89MTnd+xxZ1hByR+tTNBaZh+Z393yFSr9qAjoC6UtYt/sc9dOSlHU9OgTokJ3ZlMHQOMKFlqKLjZJjJmdjHVbjBsLparQP9yo11ebgNt1t9qXR83wbTUBG7FZdK69qu/yqRjJ2yuSlo0W8XNDCNfrs66/qs729xHlcD2HthxXh9RKzcg0fepytYISg+0z34aEg6fbZi1EHLolO68n4/dz873a8OmjpQXAmlDks7flxbK5/+lTVtIuvQMmaP+63aqORM13mtgzJxJxNX9kumcvoZiKVgwI2dtHhQ8qlvoMEaHVG1W6yzMPMzZvbJ/Z7ZNubRvGwbj13dK3B1pfvBfO9NBn1GnD66D7pH2mpsunFbcDpVr3VEgv7W3abuo1Db4XSUq0/zKXnyzz///DxXu3hcHFDp2YZK+88//8T4wx0Xvn79umRP+NoQlqnt0ntRdvPr169v2sO09U2dlHba1vud+Mjejx8/Xn2qn5RcLJlqbOkOKpVH7Qx9hpVYhnqdfbhCxLj+UllTHKcyrBAyLm7jsTvHceliRleXa5MrM9ls2kuW7eSqvuJ7w1L/dHOO5Xb5XLudvM4HSH3mZN6xS7/1UbuZoCsOF280O3cn2F9xAlfK0knZ7Q5bdeLvAR1Y7n1MqiD5kla3TbYrl05596y7U3pUvERn0J2cqY7uHs+u3zPNwcFBj0fOoc7Rr+tOHmfI65o7x/fsud2jmk6dTCXsO0eioASS04Uzh9BhJb3rB/eYAdvh7EXtbmIaPlLn3nOSbJFrv7sHSW93TjrlPOjBwP3g4KPgFp985tfr+Rnh5OIHt6Ch36wnEQZVVvdKjNQW6lX33W0euPdTMyk2WXniYkVnqy1iHt4zZ4t1ceTp6ck+Qn4PJFJK78kK6eHKXcEs9lrJuxpXdWV3fhztflcX51ZH6HX5byVqKj3ldX7cFXu6247k46626bcRT53T7LaezvJ0gcOOLLeQT86ZnZEb7vheSIOChBgDHSWcSEC592PRkOq7oNyxS5vkVqRJloI+Zxzfg2w6ODi4hhlJ0unn3bnZOcx6bsVpIMlDOVTn6ipZt3pGsmoVnUN7i1O4m6/T9drXJORYF+2ILpBo+WOMN+fSbrKrvkLBOV7HNhwcfB482udW4kWJnrrmSKbScSmeoR3R8x347ju1NbprdEa2p8A32cNZH19ZANE6nE1xYD+nuIQLIjzv6nh+fn7Vd9+/fx/Pz88/d7d0/ZPkXyVg3PWUZ6WfmN75MSvprsLFcolQdaj50Y1lR44lImXlu5PFYSUW7eDav1vW6j1z/ZT4kp1x8Nv/1W42CWYBgKZzSn8F3da8WwbHTHnMiLRbMQvi6rd734iTZfZC1yrbrUjX/dEVbF538mpw5lZ6dkgqIgWUqoTcPUxpZuUcrOGz9tVnlfujYTb/7k3mO+dfz6dyOkJqNucrndsFpdB/wutAe7fyKPwqUp+6oKirj7agzlVfuV24VV563JAvdKUt63b3qqzJzsza9FkXKt5bps4eftQ+OjjYIUYYf7hAv3auqm2pj+bhY4Aqjzt2clM2XchQ8qnSryxyOD3M63p+pe+uLoaoPJ2drsfa+J4ttTFc0EikVhff1HXubtK+5Q5d94SJ5ktxEfttFmt09yONo450IvlyK5myWsYOH+DOpTmV5lnX5lRX6uPdvtG+XfUrnXz3TMsx6s6v4N2Jp3sQKt2N5m6pFaR3H7m6VuXSwdwZiyuB0q48KwNitu2TRBGV7xh+tVmPXWDA691g1mehZ4HY7kRYIZZmLDn7+28in3YdjL8V957jfxr0/T1XDFphpZ87Z8wFA4qVRyCo28v5qGOSLnSKtO1up2kny4oztItZGe5eucCE9oPXHOHknDDNN8bbdz+xXCfrzM7MHEbNm2zGVR/ivTC7Z/fCjHw6uA9OX66hG38uyEz96vQRg0WXp/RbynMluC64hWQlNxx5lGIAkkl8355b5Eg6+l5gzOfIG9W9anecTSXx5nx7Eleavo757qY6P3uUsY5d/ONiDcWMjNzVB66MVcLT1Tcbx5outTPFWsnncHa3m5OzNq/Iznau3JeO9Etj4xY5V/PeY8x0+BTveNKbmvKkFYIZqJwKV569TflVpllwUjLdE92A7Qa/I4/02hiv28PVeUdOVcBVeZ3RowFhW0ruWVDabbnUPtByWQ/r7PJ0ebv0B6+x0kcfzbn+jDJ/ZLxHf6aAQR1tOrid46Iv9FbHXOtTR1XBF2Tr9wqhQ1nu5eivBlsr57nzNQU6zkFUx1/tTFrU6GwR67lVJzu78Rl0/a4zfA+44OKj99PBn4kVv5DHqzZnhWBVIicREUQX/xQSGVS+cOkn/XdQPZ/qdEH1FVKBZczaluTpzrvAnfGI2uuyIdUn1X/uH1TZv2X7K62ST2WHNObh4ov7gyaNW0oe9QtIHs7GaEdYkazrCBN3rYtxdny0FKulOdGVvWLHHml/OuLakYvM59Lfmyzr6iRSv1+dw7/9UTuCDvOMdGKQsBuMpEnjlMFKWQX3LzxM436nc/cAyZM0gBhs1bnZow2ax+12mhm2tPOs8lIZu3atKL402R2xxL5h/m6i/m0O9sq4/dP7YIZDPvWouazOXeqztKrV5Ulw79WgXuK1pD/VoVDnlmmp0+iI6CPMrh73W8u5FzqblRy8ZKsrrXtEIdmWbgHEBRRpQWNmi2hnVnVVRzbt6P6Poj87x/KWMu8RnBys4fTpHJyzaXFR0/PcSh2Vh3qzdJezK6q/ks+ejp1PzkfsKo+zLUmHub5Y1ZP31CPunHuSQuFil+rPb9++vZKxFkUou5aru6fqvC526LESWQ5cLKlzzheoOrX+K5skHOiLuOvueIy3ZAqxMmfcP0jOyBeHWWyn88vN+RlWYvgklyOfnNwuPXVVfc/u26psBdfXHfGU9EmH37rjiZgFGpqmkBT2rBzFrmO/C/1Hok5JdI/8XUWaYC54qG8qZqbvnPuu/0kOpefFWX4XJDCNI6FSWuZj2a6+W9McHBzsoSNSnDHfQVq4cASUpllxINLcp67VtG5XqRr3ZBsc8fUIOP2+6gwyr+p/dQi792uk/Hqd+dM1JcJKhtSeGVGyYg/+dnTk08HBe6PTmSmwXgF3+a/mV5LJLYQkW9fZIi277Agfw9NrLkZJj5tX2lm88qg5z36ZkU8kiMb49Y9zWk6RUWp/mdbZbz7up+QQA3R+a36eox/gyKiVmLHzR0herNrCFezG4d0YT0TSGK99oxTHd7LMxucsL0E/8Go/6PGMq9j1/fReO3Q+mCtrte4PQzwlQsn9dtdSoLCCZHCu3MSUd1bWvZ+BJjqnT5VlyalbTJW4Se/ooNKfvQPKBQIqkyOfNB/lUjjyx5FPqX+6PnR5d9McfD7c636ecbGO0jNOXz0Cjlji43W87mTqApg6RyehnEw+rkfHwK3E6nUSLh1uGYtX7wFtympZ6VE5Xnf3ZWariLRIodc7PNoGXCnXLbDcq650/1zeK+TT0Zmv8R668G+A2pcxclDn9PlKvzt/dxa8KuGUCB+e44L9is0heaHv0Vt5Z1OV+fXr1/G///u/lwN2LesqtD3d+/xSv3XxDBcn3A5c1pPeN+V2Wum/32kd9CeS7PpnTW7MdmRTd30X99TRvFeu/XqNpNmKnM5PuBr7c44kPbKiO5h+1T7ewlskOE7Fpbly79+NeOqUdz3j7Jx3KuGkiF1AsEJm8aY5Y7F6I3Xw7wZLj3Cuqt/GyAFJGtxp26eWyXJV8aZ3QFWdY/x6fpp9VUaQ7+lQGfTe1aczOOkeclWjjncDo/fGrlJMivm9Hf/fMQ8+Cv7ktt0LSvgUHInP9Ol3Old1dE4Ny9DPLChIOolpiHIkXf6kj+jcan6ie+xb07j28R6sEkYsW8mg0vX6QlYt++np6Wc6vaZBVF13zqO2Rbfyf/369ZWN0v7WvtO/Gdc0uw7vqi8xc15n+dxvBpkpD7Gjr1bnEMvfIa12yv4b7cxH9FM+Opx+druNCrM+du8BdEF06cB615CrR+t3C9PJDozxy9dOc750V+lUvlpEiRWVg3ryx48f43/+539e6UfNTzKL8y4F7anfaZfoC1RbmKbsjdPlStxp/dwlxg/7ouoqW+PuH+Mh7R+1Y3zszN2D6gvK1Pk0OzFN8iEoB/usG5cdVmJ7+jep7NlGDiXsGM9y7KpsaS7r/adsXR9qfj5FpHPPxW3s+/rWtN1uxaTfZj4x+/3KppnLxJNO2FvQlXElINAyV4IQMtpdUDDDbBW1K98pfcrp6lpF9xifI2ZIvjjZNYDQ6+49Hc5AuFVqLTsFmM5hTcGpts3J4Prh4ODg94IrirMg9dagy9kRPa+/ed45JSUX7YteG8O/ZLyua/6ObFOnpa6lbeeqw5MNSc6KYiUAS+dTvexrZ0eSA+geu6NTlxYyWDaJJT12q+A7jvbfbmP+5rYffCykgJBkBPM43de9AsR91zH1Xene+naklMtbsiVix+l7BsYkrjQ4daQHn0jQj+pdtnvWFvaJC46Tf59ssSu72qn9oeXyn1GrfLeYX/ncy8iLDCO5pAsqY4yfj/fp9cpP20bSjP1S+VzM5uIgl07LYXs+AlIsvesLpjmd+izV7YigLo7W/F2fsgySXAmzOcf73hFU9/CxFXfZ8XRFqFVFlBQS0zjSqSOeUrkcSDtwQQYJpTTodAC4F5tzoK+SfqyPBNRsUFU/KkNMR3/lHU/JyeejdgwyWD/LTf2T0iWyKU3EE0QcHPw+qI5JTlH3O8HtdprZkTrnVsSTPubKlJ7XfOq4ujbq96we1ydOhtV3QxBd/7rHNFwa7orVnUXMz8e71T64v68eY7x5uas661Wurkq7YIb96R7JSO10Dqim75za1TSfDbf4VgcH90YagzrXmdbN9aTzUoxSecrXVT2nH17X8l18o/rT2RvGJ4xNOriFb931VOXpo3vuKYuk41O/8nqXfmb39Z/nNA93vLCdlJm2iz5AIn900eL79+8/y+12NVfsw91XOjbUfjlbP9O1MzJKy05lpnj7Fj2fbGaHq+TICvGjx7O+qHS0/4mocrKnemeE0wzJ33AyUA5Nf4sMy8RTUrxXGLEZ6cRjt5pApV7p3HmWt1L/rQyfG5jqtLpH1Fi/K8tdn7VFfztZujYrGUS5SRBx+6IGB6UwZ+RTgYEDV6spqwYlLmhwhtgFrHT2rwYOV/GnBBkHB1eQ9K46Z7PV6FXdTSeedkSvad3cBk25VGaSGiqr++3a5NITbgu6ftTukEjR/LfA3ZfUHu5mciQR8zhnmGQQ7ZSm03pdWsqlMrFutpu2xtkgbcuKn5Hs0XvZh0fV1c2FW3yug/9w/Ic1pICxjq/sfKq0XYzCNGpL9LqST5TB1a06xZEfblzUOf77qiNAFPp4lxIu+qiSysJYIdm7QhfL3aIjGEeU7uZiiMIRBGq7vn379mq8cHGk6nV2Qkml6tenp//e/6S7zkreSt8RXum9T5WmiyFnfevs8r3uTWdvOp3W+XwrupB2eyVdknWFY6BvwLyd/M63SHXTP3WY6ZFZW67e75t2PF153K5T1nrsSBO95px+p6SvTopbHaBEHLF8dW71mnNM1QHf3fHkZEjPdVc+nWBU0IVEMDmnXh8jSSvSKgcDB+0XKlfXhxo0JHQBH+/BPcinFcX+Nzvfn7ntn1n2j4TVQNTN0RmcfVC9wlVEXtOPpqe8Tscp9PfMGVxBIpjU0VWnlf3l7Mlqf3bOi2un2hR9rIABjNqQRAhpfVzNdoSSHqtNq/RjjDd2zJFlJWfVveO4pt96XtvlyKf3wr3rmvlF74WVdh0C58/GlcC78q3qvPrtFjPc7qYx8h9aKGjH6Mcnu3hlTLv6Sz/qdxEpjF9Uf9JOsT1aJ4+1j1LaJL/aQrUn2h/uSYv0RIa2zaXR8lw/Mg/vS710vGx21efys2/1OGHmO6WYZoVs2YmHZmA5rIN9zlht5leRdFrVCSkmnOVL7VpNl0gnlWn1fiT5Zn3GPr/iI2zteEqOzwoz5jDbocQ0KRiovCwryeNWpu8Bt4tIj1Up6GBwCkqPrxrI1TTJsdb77cgm/T3G27/3dOQT5XK7nbj1ta6lQEKNAeXaIZ26sc102j/3xCPKPDj4LEhBd/0e4+2ChyNuOltS152NoI3RYID2iu/fcHpbSYlC96jWis7WdihZT7JLHe2Swz0WsYNkd1O6WXtIArFNdLodkVZpSfQ52+Ic9yqX9kpXs0lEMQhQ+Vy/zgij97Yz7w3nJPP3FQf2kfjM/f5Z5f7dSD5hF/+U7tF8Lh5xsYqzQ6pXVNc7nUtdoTagZFPszLEUW2nZzs7oAgAXAyodd1gRLo5jP6y2SWXjDlnmp13Qc4wxku+gcLJqH+hv1y9up67eD/dOKcZvHAOJzHHo9Ajvy6P090zezv+o/J0up9/hynhv25SIM/Xf9LvSqh+j+TtwjHaE473u9aUdT490Ejjh9Rwn9oyU0jKqHP2utjAd01yRneWP8VZhKXOpSsJNrB1l0aXpJqA64ZSVqxVUhGT+q711jzRfXeN9dv2jSp+ys8+4EuHapxPXKSeFC2jZh24sJazcs9W59Sc7liuOxEdDCsAVH1Huj4xuLigJ4NJ0eZPDqCvPtENMU1vnZ06QW53qbISTyzl4LuDQT+0I1Uci9D15aVV8ZxEpOWYrDluyCUpAlWOttoc2kvaVDjkdfRJxWgYfv9ByKVvl0/LUl+iIvW7BY8XOzHBPPfMIP29GQr0X/lR9/Vnlfm+4+dfZceczajksW33SlR1Pms7ZnGQLVBbqso7g6WyXk5V9obamvqs9//777yv9y/5b9ZXZ7hWb6+AWOLg7y5WltlvthsY6JZPuKNbyOL7G+O++kESoXU5PT08/FzzGGD+PGTeWTat2VD0c1ypTdw9cP64Ql/e0EdUOZwfTfV/1rWhPWY/rj1nbuuvOzl3RzcoPJJmvzIlKl/znrl+v1KXYIp7czefq81WwA7iVVI+dUqKCqjJmiiVdv9KZaULXb30RKrdZ1sdtk5w9qrEik8riyqLMHbvPYy2TCtntSnIkleZ120i5Oq1ypJ1mhE5gp8xmEy0psYODg/vABeX1m9e6nU8OyXl31+no6kdJJ9q+tFsz2YRV0knLdsEECSclWb5///5m19OPHz/Gv//++0aeVSee/ZjsE51gV5baPrdwobqd91zJIj1XfaTBRR07AknL4nl9LEPL00c13H1MNoQ2h330N9mZP7ltB58DM53ndFfSZ05vO7vT7aBVQkptTYp/Sh79XtHjrjxeL5nYpkQ8Ffmi+lYfyRtj2BgnyZcC452YLZFHFY9xt63aEl2sYf2JzErt4/1SO+Lur9ojLY+LHVWGluvkpX1ZGfeuHC2P51WGe+n2FK+5uplvpw5X7mof7fRl8nFdOezLmd9I+VlG529eLXdF1xB3+Ve7W3FFSXbOb7cl1dXnjMQtmJEWHEy6XbV+a1p1yGfBVSfTjO12TrEjdSot5apr6qi7/tD7xHbpClH3aB3LcvUQDGJnypPYmZwHBwfX4YxzN7939eKKnncrzZpXdZNbfKGO5zWV3Z13q+Rj5FXsl5dfj1kU2VRlfPv27U39PEdZVjCzpTt5eY322zk6am+crahVZCWjynHn4gpJTE2r9deLX9WZo82mzI5Adb+1X/50O/Ont+/gYyMFsYwjCNoat0he51WPdUGzLmSQdFL7kmKakku/Z+1lOU53k/hS3cQnHniddbnF5k4vuj5z+t99Ox1LGapP9XE1BufuX2Ypq1vIcJsFKu0Y4w3ZpGWrLeGupjpX7VDCqetf9gOvJczIJwdn034XVtrazRfe2ytIfbhDEu/AzZGOWKvPjOBz+a9imXii48z3WzBNTaCkvNNjclqWe9TObV1Nj0jMlH6SS9EZC/52wVIinPRT7aWTrWXUubR9fwVOYXNAuslAZamPvekuplr5YF5nEOn809HXvFTkFWBpevciXyX0GAC6e7RisB05l/KsMv9XDcBKnvc0APdo5yPrfyTubTz+VlC/ua31db4Ddb7b/Vrp6rquQnP1udJwt5OzMU5/q84sXUkZXLtZj7OJrr4inkoflvNa2/aLPOGqNG3Eiu1MwZDeu2S7aA9Vd7N9+khB2QCusFeZ6pC7hRMdPyScNM8///zzakV/jP/ILLUnem+5C0rHl9NPK+dmTmqXXn/z/Iq+XKkvpbtS9lV0ZSX7PtMhHyV4Usz8DR4fZOjuRWdnfvz4MZ6fX4dITkfqN/MruVE2hWSOEkzdOHayUvfXd7erKOl1nleZnP2ssp+fn3/aFrWjtaP25eXl1WNk7jFo7Z/U391mgy4m0zK03ur70uf6WHql03NarvbVzJZXHyX51L5VH1aasnH1aF7Zb9ZZ50pWZxP0miP59J7SXmseNx4YYzHu4j3Ucklgqhxdn86uaf/qMeNvrZ/zK+nRNLe6WJJj1fltK3BpGROvyDOrV8dhZytndThc3vHUOUIKXWm8F5zi5+9Vp3mMOdmUykhyJGetbiAfs6s8JEdcH+tq7j2QnFqdpKo0HJOfnFy3o8ld17o1aKjfNFJMr9d4zLY6Jzw55q6fZmm6/iNWy/ndWJlD7y3nR+ujMdaM4MEeGNhXH5OcpnOxC+rYlIbOiftU2qRn6AA5GVjXGOPNYw/Ovmn55dBrf5VuLEdW9a1zVq9iFtQ7zO4h7U99u8cLNW0FFmo/UpDn7Mjz8/NPoq7qVZKr+lIfu+jsZGdDVq9rmS79zBat6qJH6NlbxlVX1m7b/iQkQuTgNiRSd7d/GXjTViQbQh2f0nRz3NkYZzucXUuLLFpmehwtkQ9OplS29tusb1euuXuX9K3WXWSee+qizpcdcDrZyU9bVfZFia8qi+980pi6ZHLEA9u9o8+1DStpXTr6PyoLfSHnK62ia9cszpvd/1n5V7AST67EkJTvEbZ1pR92+2T7X+0cdPKkCTbGfTplpjhZDxVEV+bqb3e+O3ZKVp3Tcv6VeNJ0OkG7dhCrBFVy+DtH1fWR25bqyCo91mCh4FaZGBCoXC5Y+PLli328hOPGrUQn5beiLIiVtPdKc3Dw2eFsRSKf9Frl3Z2bY/SPZjunuzvvynf2gKSTXmc+3X3FgEDTa5m6+1R188vLr52pGlRwZVf7pfqZfZXa24G2iztZU1o610xfep92kvYh7XTSepiP5BMJUGfHab+1XMp/D8f74OAq6f63ws2/lTxubqb81P38pF2zXGTgo3ipHvrviZB0cZGTydke14dOFvr1WndHaKzch51xntK6JzkKau94XXW59pl73LrQ/VsdbZS+i5BPcDC/tq92S6kfQH8qxXpa1syPSmMtgT6KK9vNQRd/Ed18dGk7GXfO75S9AvUVeO4jIHEBV8mnu73jaYXk2DGMqw5uN2CoXJ3iS+V1cqb6mccFBPq75KCzS4XIG+4GqcPu7qjZvVFn2j0ayMffVI6C+6tX10aF9gP/PtQpZiVBqaA1SKWchJtsbuzMWPSkQO6V5uD349yX+yAR3O6xXDo+q7ZllTRxQcIY/jHxzl5RjyhJ3snBYKOuFTHP82oTvnz5YhcCdGVViRUGPpR/Fuys2GlXRtpFW/VqXkc0MRhg39KGOvnUTjj7oKvNSs4xUNG83WKLa6dz4j+Tvv8scv5J6ObMwRwkg52uSAEyj2egLqDu1HNcYFA9r2lYLuVNelXr0DKqPpWnHgMc4+3rVboYpK7pIrLujHLECOMCwsm7Chf7Of2v/a4LXqyPCyalw9Mjjrxfzs6ovdd3NFZdtZBex2XL9d8Eky1J/cHfs/Hs7oHumF4hTlaJLSenQ6rTkSJdjJX8BFe2/p7FbdoOtm3WF6mNeo765JFgP74L8bTi3JMN3slLUDmnR+Lo+Dsnmue6wb1zE7tdVDRoY7weLCRKWGaV4UieVew88kDl2xmWlZ1MfCeHls22uC2sY/wKenTVQZ189gdXEtyqgZO569ekHKg43D12QfIszUo593IwdxX734pHK/SD/5D0oF7j/O12PaX7Rief11xwwGvd+QLnTslHAl2h7XYBh9pD1e+dTVM9W7tytCz319Iqb8JsXrjrdNgT+cQFGHXw6zqDRZbVEVsaFGk/ajDAsefkK6iNYr/t2Bn9/SgcffY5ce7bfeEIh/pd16kbeA+YRnUPy066vM7pTie3wKEyOttVukv1qsLt7q3fSnzQxuh5rUvbr3Xrkwz17Xarlnyq41d3dBFpB6srgwFz2UAuZOgOV/1TCSUsGed0hAP7T/ur+kB3PlX5hSKfqi+5C5dtdPGXG8NdDLMLLSPlT2TNrXD+Vgdtd/J/NJ27x13Zs3p34e5fkv1R0Pa/C/FUhauz7xqbyKd7IE3kFBB0hJSbXCvy7u7GqsEyWwV1bWKg4pRJwm7/q5z1m0FC3XvtB25FdcaHfc6AQScijUV99EWyLNdtk1VjrkZFZUxt03Sun9I1tiUpmHulOXg8VufQuT/3RXLoOZdr3rp3CSYjraBT75x8rZ9luACBeUqWgtv5WWWxjk5ezaNlcfeNEvFsW3K2HFZsSvILuuAhEUQqZ9opptfrt6ZJgQzvodahMjEfnX9CHdk61sAs2ZkUFHx0vbIq33s5xn8rPvo4+WjQnRrUh6rDEnmzGnyuBoWJaGIZyQ7VOfXh02K9k5Hy6jmSZgrdQUviq+zcy8vLqxdn6+KHfqfYJvXfii2q+lKbtR36mBvts74gnW3tdtZqH6nMDN7Lr+EOJvY97WTtyC25SfrN+oxxpZ7XfnBk3SoJo/k6eTjGZ8TGSt2U340tJ4OLvRzpskrSrdSzAudHvYdtTX1wxe7c/I6nFUeUAcQMdPadwtVvRy4xP42J5nd1d21KZaRBpYpYt+2rY8sXo1ZeDvqdR+d24RSQ2wWVVjcKqoy1bXpdy3AklKat6ySyqoxaEeAuJ/avBqw6YRj40SFxx9pnKiv78koanp+Vcytm8/ezOrQryvheQdNn7aOPBs4PZzvceQbtM2NOe5D0zyq6YIAy8qMvrVa5WdaKvNSRqierLl0hTc7cjPBe6a90Pek6Rz5Rdjr73A1L2+Pkr/Jrx5Prv+oHXeyo3WJlxxNI9Ln+c3amuyePwHs4qwf3hxsjx/7sQ0kEDepVH6ddNHzX2w5W8rkYpruerlGXz3xItTtsv5Oj2qI+N+OVIkPGeE2SqJ4ukmRGPiX7OovbuEiQ4hXGBymeVNmTnte8nXxq/8rm1D/Z1SN36q9oDJnuJ23LzC9hGSq3iztXYlEXL2n+Fb9g1R6mmIzXO7Io5et8pBQjdmUSs3uZ2j+T/dHQPtjF9qN2Y1xv5I6AO4q5fvNcUprdVs6u7lmaVK7eoHJ2S8HQmU5OLVcE7klAdZObq+ndfXGPOdCYVH1ab2cMaQwceaRBgeYpBe2CFG07FYdTeimAcQpvlmeWhuffMxg5OPidcIacDq0GCSsLBYQjb1aurZZboO7kHykwKBhjvHovk37cP9pVHUmfukCg/j66rrv3R6gu3dU73Qp752DtOGzsBxcIuG/K4P6+m6i/Cq9+0UDABWbVB3oPaK+Tnekc5oMDhfoEZ4xcA3fGl/+tC5kJK8E09RwXS7h7psOMrGJdJNCZ5unp9Z8BuZ2+7riLe7gTrIgTJfKq3mp39bfKs4qZzZjpdsYDqs/dUx/aNvop+s18Srx1Or7sS/Xb9+/ff9qfeqSuyqo46N9//33T5rLfHN+qM7gbijZIbZTGTokQdO1PY9r1IctT+8iyXTk8vxMzJb/EPaap7Z+V341HZ/t3Yj4n8yMwK/+q7bn0cvGZEhzj9T8RXcVK3lkdzlFP5Sej0cm0IqMrl4SJGqD02MFKv1+Fc36piCgXJ04ne8EpdtanBoyr2FytT49nJGPLxyeSYXGKONWR+rLDvdIcHPxp6MgnXleofiis6ucV3a96Z3cRppzGme5yiyWzHb1dGxhMjDF+OrfOIWV+/Tc8dz05jgldYFbtd7ZBUU48FyEKjnCjzE9PT2/6QOHGUC1kcMW55NWFJCcbyafK39mZg4ODxyAt3qrPOcbabor0VIfzL+vjAt2dx/gSNCZytqq+lUCnndBvHrs2Vr0qOwkwF3jrPXDvPUxkzgyzdCpzeuJCy9CdPykGZHrt07JZbpf2GL98Fz5qp7KRmCMJ5O61uycFZ49cP3X+ENudylD5nC/h6rmHTbzVnjpSq753fcBdMLZ21/X7vXwHdw93614mntz7E6i4uxVEnndKtsvLiewGqZ6viUtiioSYOuRJ1k42hT7rzLaqbHpdiRhVUu5RtMrLctw2SO2TDisDmmWSBNLrZWzSy3O5Q0GNDxV3erZe2Xxeq+tPT0/jf/7nf14Re3qsfzfORwdLLp3wVDh8lEMDEPZZmpQrzPkqu34LPlqwsyLPR5P54D7guymou8d4vW1eSQadr05HUoe64J9Glc65oiNfuMJZZITap+QwpMURbu2nk+psgzq91TZ9qXbprUpXK63OKdVV0l1na8W5dLaOv1UOXflVPdk52yoDd5ERDBB1Bf/bt28/y31+fv5Zhr6AtnaRVRlqg6o8tR0piHB96dp1i61Iee/pVK/I9Sgn/tFl/06ksXvQwxFF6tvVnKSOqPnKx+0caTIjrXjt27dvP/WJyuJiGc2rMmiajjTQR40dUaF+s+uHgvrSWl7ZlNKFlIuPg/3zzz+v/HC3wybFKyT+Ur+7fmJcoTEBdz7pbqKqg0Sl1lX3LO1YLlnro4/XVR3fvn0b/9//9/+9kV3zqa1iLFI28du3bz/te8lNu8OnSdQvco+yp3tSctKuzGwt02pZWp4rv47TY5XdfKRNZZvSuOvmtKtrZqNT38zqqbE2hifUXd9V3q5tKf691U/Y3vGUHnsopE7SAXzVAeicRIVT0FQyXXm3yOec/26XT/3uVnrTTiO9zvKvOB4svzOaKhONTimpTmbN556P1nZp21SJunpS+9Wg6BhOSk7hxq7rKxcQPAorY/SjOZ+fUeaD34POzqRz6oAm3aXztCPPnWPI327Od/rOfRPuhdWr88bpPae3yhEmyafOpf6+QjhdtaHM6+xid11tSmdPNZDjohadYD4KwkDH/U34GP8RUvW315VvxX/Rtrjfzo96T1LnSjm/U6/fq90fDaldt/jYB2+RgiztZxL/q2XRrpBQcHn0nAvO67ibcylfkrnTNal/qO/0t/r9Xf0zn3rW351fn9qUYgmVe4y38Zbrh0rPfytk25TsLPui5NM///wz/v3335/+QdmfsuO6CMNHG92COG2Jpk9EhPbTaow5iyMpF7/T2OhsoJs3bp6typPatZtnhayZlbs63l1MvhOfJp1DvbTqzxCXHrVLjz04xfsRjeDVzurglAknHXc3pUk8U8KKLuBYQadAZteYzrXXkUIquztWuD7SgFGfPy64VfJEhHVKrWtfajPb2aVJ9V1Nc3Dw2UFiyJFIan/0t5JPJJScQ8/5XHVyrpFgp06b2UCVXa+5nVopn7MPJNtmoB4mOaPBgLZbP7vv4Uj1r16jnF071KlPpKB+9K+x9bxzUot8quMaR7VDQXc3KdyO5iTXjFhasbm34tiZg4NfWIlp1A/dKXeM17tsCrozVm1Wsi3p/E4MUN8rAbXrD5dGP3xszC0AK+kyhvfZWf5Km3luFl85HegW2usc392oeSodFztoB7QP+Bnj7S4WXQyq77JDVbbb+aL1zGIglzfd+3vAjSPWnWLJMV4/jaXf7ngF6jPO0nWgz6ll85x+u/5ejRUp14p/6uR27bjHPb/pX+2S47OrIB+FlQDA3aQx1v7dbqU+Ks2XF/+va6WQnAIb4+2/Fc3S7ODKPdF2VBmcVJom9Q0nnzPq9a0kUjn6bleE1q/ByBjjjfFj2WyLytSRT65/XBpHaPG466MTFPwenH5/HziSaYy3u3JIPmleRx44PVLlq/PrnA0950gs1W/1nR7F6JyMWV+4dEm3KDpbR2JP303k9HC3nXvlkfUZko/hbI0Gb3zMwgUs1PtKPOlvLUfrV/JJZSvo34Vrn2j6tLt3FvQ53c9zzkat4tiZg78NqsPSo8rULU4/VT63aKFllV5ieTzPcwz4nG/Jb+Kqj9/ZF8qTHvFRHai6nI/1VVtTzNC1IT2G6ORJZalstNlqe0ii6UfLUntTj09yly37r/rix48frx55T/qYfkiV8/T0+g9DdGFEx2sq2/kezt6swM2Z1BZ3PflQvNaV42ynGwtXbOhKuplv5n6n+dzJxvSdT5H6Zzbf74Htf7VTxzo9I03n+1FQRa2TPA0qxzZzIFNJ7sANmk52t1tHr2mAwzRUBlrfzsrLGG8Nxk673d93s92zZ09XfnOiPD8/j5eXl58r8Pr+Df0XIo6/p6enV8FVyaXG3gUBnWF3So353HGl1WO2OaVZUQIr9/GzBhhXHKmrSM7cwf2hRFP1uzpJY7x1Bmu7uTqyBf3XUNUHs4CBx9x9xV1WJLZcMJDsUv2eEQsr/Zag9rHKVF3Ja9oWLV+Dg1SP+83zySa4eVb9p7vYuICgfZ12ymn/13sUq9xuJbpshrvu7lE9Xlcr3FzBd6v8nY5Jj452pNOOvurszMHB3wCnM8Z4vTih6ep4jNdEt/5jG31itTdqq9RfrXnnFkO0rpKtznHez/xqB5fGEWIFRzip/lBd6fpW26e7dbSt3cJ1YSVm64Jx55+7b+ryjuSr9G6RQ3c+KZRwqjQV3zj7XkSVfp6eXr9Pqwin6l+1SWo7GZvoGNN7qn2wamc4hlMMkwiwFG/p3GLMpcfOHyO6uCqVvQo3j1luV6crL51z45x9n3w8V/694kxi+1E7bcDskQfF7lbUVewoVFWGVCyOxFotv8DAxyk0vdH6XK5OdG7fdAbGbcet/DuT4+Xl5c1L0dNjDSl/Oe98B4aWUfWkRzVIBBF6T1h3KWv2p/s7V1XOCj6qkeS4ii6o7JTh1ft6cF88SgEfeHCn0xi/+rfmeOmEmuN06OnE1rdzhNR5U/1bsriVUBLWzolTODukcA5A5xC5dGw7P9re6l8GPFoOHW2SJU421zaF1tHtZHKo6yRxSOConaEtd6STW4mutmkQoG3TxxWU4Cz5y9FXW+RIJBfIpvHj+rryXMWxMwd/IzjOdSGDaWiPOG9VdzCor/TUn5W3dvCrHnL+M3W4+rYJiUhZ7ROV0aVJgSvPK2mhx6q/GfOs+OLcaODavtJuvc7XeJQNULuquprtJPFEO6OElI4hfV/Tjx8/XhFI3J1Eu+7IzC9ffr3vif2nsZgjBev+qC1yY+iKX3yLfblip2YEjxsnyQZTjntixe+p45Teyb1KJDny6lHYetSuoA5eRzZVvkc3hERRp2TdNa52XiWeWIf+TkEO30WiSm6M8eY6gx1tswuqroB/SV5YWXmodNwirAGD9gsxC1oUDPJKUVY5jozS4IP1av92RFf6vZLOzSP2RQoEblX4Dp81yLjiQB18fKRgIO2AqpVEJZ/GGK90gjq3dY26qOY9SSfVx243pBI2bueUw4qjrHKuQB1g2gAXHKS6afv4OMQY3lHtyuTxTr76TedJgxdXl8pb4Dudnp+ffwYBJKAUtbBRwSGd05eXl58vflWnX8umLXL3iz5HCraS833FV7mHnTk4+Ixwuy3LltQ8V9+t9I3aiKenXztMlHxmuaULKg8XT1Qe6gI+/aDljvF24bn7LiRSJ9kct3GAZIieV0Ik2Z/qF417HMml90JlZf/NbOqMSHB9xL4nSTbGeLXgrvGF2hN91O7r16+vXjbOfnN/nORiL9qQkkX7qPqTCyPOHmk5rr/c4tuKb5HsirNfK2VxXiakGGwltteYWttwFa7OFDu66zuEW+qjjoBy/lUng8p4xe9YJp7chOc1Kgw6aPfe9bTTYJJL6XxKs7L7hYTNzs4hTUNFpzIoA6+YkUI7SJN6tu1VZUvpKPeKEnDQPmLwoZOT7yyp8xyLKcAtOPIoTe7kxCtmioRlnADg4G8AH4Md45fzPYb/E4f6nY6pi2ZOT+kUJV3ogKh+TtfdYwozp8k9TkYkB8IRTTyn5HwHBgEqk0PSdyng0fun9tWRSU73JlT/aqBX58vxryCRxBPf+VTgYgRlKfn+93//d/z48WM8Pz//DFDd7tvkXLoxneCC2ys24tiZg78ViXhy8cEYrwlv2qk6V3pWy3EEhsZD9c1zjAMc8dTNV/rWjlRX+QjaqZSG30pucNcM5aOucztgVZa0cN0FwCnY7/qys2P0C3QRW+2MLmikRQ7GIGor1O8pQoryql3We8A/u6ANc0/bsF/YBxzXq7Yi9fksrZ5bKSMRI87fY30cZ9pWntslo1KbeG0WB86w4ifRX9yx9107dnHpX+3GyIPz3uTSDLOGd9e7bZpMs9omOtSFREKRWEpkhsurg9Ll33UiWbdzbmcr+87Yuuv6+8rgrfo7QlCJOle/Tv5ua+lOfx5H/uDgNridNTUXudronHYF9ZkSQk7HMjBQOSqvOprUuav2r3PGC3TCV/SQC1D0/BV0ur6zAVeRyCfFrC3uHqizz+CgPi44c+2pIEGP672DY4xXO6DGGDZo0N8z20sc23JwcBv0UaMxXpMbzgYUlByqa9wJ4h4jc+DTAZzXbgcQjzVvYUY6Uaetkk+8Rpn0mn5WbILzx1eIg67srs1dWhJd3YI769DFjbIptajR2Zpqs+6gKjtSZSjBVL/V9nC8lTzuX8AZG7E/2e8uNq10u7hqv5y8bk64NDsEmOZl/bvtT2RdF+Mnf2c2hl05sz5K8/eeRBOx/ajdinPtggENHOpGdDt4dmRy0JvtdjW5HVD8nXZJJRl2BiQJGj3mZHeOMIMhh2SwEuj4pjavroavrN47eTm+3KQlg69t0FVmtq/K1ja4/iulru/S0n5ZCbhmaWYE1YpS3anvKu5haDr8KQHUKjF5kKGEA3fZEqov9LfO8yqrzqkdKh2hjq4SW2mFuKDkBRcoOsNNG6SgPdH371U7+ShgfSu5VG3W6/rYQ8lXH/e4QK3i63nVl84Zmo1vF8hpm2gX2QbWwaCvynAvA2e/qvPPe8ggku17eXn5ucOhVrSrj51tcu/aqHQcp7Trrg86rOjrW51+FwC8F1xdJFaTX7Yj5z3b9Agb91Hux2dF9ZnqOL1GnUd/V/1wN+fSzh2NdzQOqm8+/uR2RhWo++o3d/ozztG82k7qmW6Ha0eCKXGj59R+6EKv7hhTGV0ZThY3x2dzgPebH01DAirNZ+3LbqFDySiVQ9/1pe8pfH7+L1TXeESJJ7UlLqZ0bUo2vdrHnV3uHif/g/UkQkTBR1U5vnbmGMcAY0jORx2zLM+VMRsHmp7HbINrbzdnZ3CLr66uJPuu7bxi2y69XHzVqLm0DAhmuJKGzrPeRA4sOn6FlG8FbnB3QZBOfP7TkCprp1DqOA0Wd84pAZe+G1BuVZqDVPMzSNRzlJGOP6F9QuWZDBDPp/KpiNN9d06KkzPlWUm3qtRucWp3HVRt62zMrZR1D5luwUpdjwgaDnpQh7rFDkcuazq36sy5mhwadUTSyqtzGKiD3Iq10znuOh0pOj6VZkYsaPoObBvzJLlnPsFqAJDs1sw+dfo66ffK9/T0+l/tGAxo2kL1TZFNOq40uHCLHMm5nLV9Zo9dXmIlv5sLV8pZkecKOA8ceH7HZ3X1fXQc+3QNjlB+enr9/iUSSySDCikoV79dbQkXQahL1WYpsZCQAupkQyqttlvr5nn2mfM/nT3UNCt2qPOFuxiGxztx445edDuxXDoXbyoJVb95T/Tf58q2qB2pfLo4ph+eIzFTn+7PLup75qu4c7OYJ40zYqazdaw52+j8qFSGK69rn7PXszHkfLguf3dPnFyU183JRHKlMlaudQRWh8uP2n0EuAHglBDhJny6rkpiFemmU25VxMrqc6dQXdc28SWlMwfRGdlKkyaRUyg6cdJuISoVpxw68jEpDCotPV/3SJn/MtzVV/qtadLuJ71PbPtKIJDk5fV74d7lHRy8N9zuJXXQx8irW0oUOX1NvbzinCYHiAHIiuO/Uged1VSm2g86Jqx71r5kpxRsh9OLvJ5srNvN2qHTo3WOutvZeD3n/t6aLx+v8ovc1Pdm6L8G6XHdDz3PsUxb5fqzuw/O+XxPvf/edubYtYNHgPqi5ieJYrUrmnYMv/NHySXVCbqjSR+FqnwzXeiCR9X5nCfJBjlbwz5IOoY7f/S61jFDCvoTHGHhYgDK05V3Kxjn1DfjR+58ov3hGFB7wZ1MuitI/1BF39fk6qM89KXcOfbV6v25ihXyxo3vJE8n38o45Tjbjf/YHzOyaUWe1XQdB+H6jfe7u48rfsoMn4Z4SspSr3NrqR4nxeSUL53WzolO4E1OyrUjbwodOUWjRmVcv92OI9az2i4aIq6C0EDoOUc4JRlmsnHXE++TrgA4A9F9c+cZ5eoCUhdEOEOZfu9O5qQc/2T86e372+EeKyjoWKeTr2n0+j0czYTZWNTdVipbIdmjZOdI7pQdYBqVi9vp65t5Z+1xTmpKRyTCiWmdbk06N7WRdkBl12P3YToNFL99+/bm3R36N9jfv39/tRNKx6ULXLU/3Tjg2E3O7CPHt8o0Ix4fUZf7/V44dubPhd5bPkmgtoMEDhc21Fapv6/+uZ6jr5fSdem745kvWWU6W+P0rItlXDzhfN1UP8tLcPPe2QY9XtVHSafMfGi3aKDX1LbUdb7jiYsdY7z+l0AlkPgycpJQ/O12Oal8GhNqWsaUBedvaLu7eKjrx1la6n+ed3Mkxe2UvZsXycbtED8pT+cvMe1V28M+dG12tpvjOfU/y7gi54ckntyEmaXXfO5cfRwLPMZrljoRT4puB1Raha5rijQAnGPq0K2ckmwpZ1jrVZm6gedkKziFRxmcI+mO9Vwa7C7Y4D1WBep2P/FfHTQN2+J2Qrl7QgXorulv9qnLwz5x/aFldGkPDj4T0mPZPO/mHeeF04k7cA5yV98VzGxO5wh1zrDKtyrjbCfr6m5Xla8LGPi7HGr3mGMXHGlAlOx4IprqWIOB+tZdsmVH+Yid8y+0n5S8UnvEtAwM071k+zu7fRXd+J7ZcCfzSn0s39V3cHArGIg9Pb3dLcv3P9W3LvRWPh2vqrcqfeVXYmEWJKZAXH+7hY0uX4H6xelTZzfUP3aL3Zq2szUpKHZydnB6ckdXJBmTrhvjtb3V8eL0P+0LSSfts+rPeuROFy/K7ug90F21Wp+SUvq4nvMRnC1PsYbK28Wut9igma/l0ug4en5+jjElj7v5pmlX/cbVebeLHRvo/LId+7taz065xIcknhQcLDpZnOJMaZyzWXnKIXTOI7GyY4cGh2UxYHh6enr10jhNl5RDIqZINrmyeJzIKKdQEtQguz7pykiDPQ3+MvQFJY+0LdXvpcB15ZrEEtus5JTKngz1ChKRtDJ5V43jPYOOj4zVPjv43GAgoHrG/VMQAwgNChgM7DqnTj8lOzQrS8t0TrMrTx35RFwz+NC0ZWdW0O1k1TSUgXaDCzSz4KBz8AszJ7AcbgYFDAbcb+ZjP6egwl2rMr58+fLq3R1qW/jesTSGZu9G0bY/Au9pZ1j+32TXDt4PnFMkrcfwgXTy010Qq/4/F0hJqj8KTucm20VoXMG4RfVBtYNEFG0IyfUOTvd37brSh67/eT9dHKQ2ItlzxpvJVqjsarPVfnQLG/QVZh/tW9pnLr6v2CaNi1aIl0ej8+/cOScnbd095+dOeY9adEn+5iP1kOLDE08F5/RzAur5OnaOp5bnJrJOeBoJluNko7Nf53idgYGSVVq2rjA4JVWyMrBy/detQmgaXUEpmdw9oEHmdk3X/tRv/O12lXXElho9/vtDQVcIlFiqdOxrZ/y6XWaEM2ouj7unhBtnLMvV9afhT23Xgd/ey0cZqMv4uLHmna2UzbDr3Dl74vKQ4OjKd33kbAfPlR5j8EAi7unJ/wucBgm8J66fnPPJ6yxjxTl0JFi1JznBrpxkp5XEdLa97pMGHPzoS4D13Rtqe3SBizuenJ1x40vtUwqY6vgeoG1JjvDMYd1dKJj5KO+Be9uZ95T94P6gbpzpaJ3/aW7O9H6nK5MsKe/MVnW6mPal7AIXQ0oHlr6sb84l1ac7uiHpN+2LMdaJ+lQX9VzqG2eDUl92cGQXiSddrNDdVupLaOxW96PyqK1xbar7pXYoPUrJ9jINfY/VPki/d2KcNDY62V2eq/r/ar5U5z3sEMvWe9n5dKn+FMeu4kMST84BTumYx113StbVxTq7XVUsX+HIJCWInPJ2ClonrwZeKdjgIOgmvQ68juggsaXt5UTWMrlDq1PELtBIedxK9Bhv/w3QbT3Wflbjq0GAttXthNIJt+LkO2V9iJODAw8aNHWAqBeUOEmElDr+Ou92CKjkrPOa/k6EUxcsuHq0vZS9I6C+fftm7YHaESXq1eHsHBHn/K042c7u8rq2b2aXNK3KxrHRgSQdbXC6L9V39bt7x5MST/reDrWPru/VRukY77Bi0z8T0ng7OPjdWNHnKW4puOszu7LyYfpuZ+ZM1n/++edVHOLile/fv9vYRdPQTrmFjsKK3k+xgd6PGdyrQVgfg/Wr0JgvkXHuPvI8SaX6fn5+Ht+/f3+z2KHpnp//C/frTzIYz2nfpY0Gj9bJM79sZazoGNU0O3Zx9b6vlLnTX1VeIoluQedzUdZb08zwIYmnBKd4Zo1216mcqKz12Cl0l1bREU90bElAuetjeCWpO5wcyUTyR2VVB9U5/UzD8jQNy0/kk5Mjgffa9UkKQNiuZOBW5Bijf8yRWHH8nUG7Eiikfjk4+OxI+mkGbh9n/hm5kpAcer1GUsrttnUBAB1mLTvt+lT96z7l3Gp/uIWC1D62PTlCifhZdR6TTl4lUJKtWkEFP2qb3e5WvU9KCHG1WUkoTdMRTHw0xdk0106m2emzXbynnbmXbTw4uBeoo6iTV0iOVJ6zCSmdpk96ehavpE9qR73YWudgilnSo1srbdGyFV0McU/s6Bnq4u4pjALJBEcu1PkO3GVbC0w1hnShjTuk6h7povyXL19ePfWxa3NTu2/V28yb/EBXD+3gjhyz2HJWd5L/ajmV74ovrOXvgvHzvW3wMvGku0HGmK9AK/S3vrizrtHR0/MzBZkceTrz+pvnqKT1pW8zRa5yzBSJCw740je9XkpNAwdVcvpomCoXrTNtPWV7KCPTqvNc5VUfdAqI9V+dBAQnYtXDl8BqsFDpdJuq9v/MyLEMtpsKIgVlK+1bhat7Byvpu3G+U857Y9eReA98xH76SFA9c9XQjvF6xwjPzaA2xZFCyc44W0FiwpXDtLRDKpfqtGpT9ZvajjpXupu6S21GXVeHtMpLq5/sL5VPz3Vk1wq0L1S2QppPnQ9CB/zl5eXVCnw9jl2/q4/cY4d6b3U88HEIPmZX56s9auOVAHP+kbNRqZ9v1Tf0X9zxVcf0d9iRRPI5qN8zS3ePNAe/DykWSMQ/X8vAuKWO9bwjlxTJzpQdSLuqWGe65uSYpWV/KMGUbA3Tqx1RwoNPFTw/P8dFeN6rrv947FAyVd2U2cVHjuBwaajDqUPUdmtetp33lLa4xkaRS9wNxby60za9H5NPqWgfJf3PMrRfyjY44o19p/WuQutg2Y54SvWpXdX+d7HcroxjvB6zaaeji5Gdn+faQh8hgX5BZ29XN41csW3bO5500DqG26VzStwpwE4x0xl3CtJNVF53aTRdUvLd7iiVg8qEN1ZfVq0KvPqs6urIKdalEyMNFm6/dGCAoUqja1NS0t1kWCF4ZtepcJyRVGhfkkCb1eP6v5uA2v6OsU6k2g5S+fd04p1Rpgy3ln3P9I9o+604pNM6yn6o7bjXfRgjG08SSby+6tSTcNJjLmyoU6gyJFmcA6yEUqX/8ePHq109lUbff+ec5jqm7RvDL2Ake5AcePYn20I4B3CGzodwxJMGCmon6jESOt5aZvdxxB3tD/t31ifdPEgOfXKgZ1ghh1YI3RW7dAvRnOq6Jd299fV71Zd86IPXSGStW0xgQF35Cy6Y7/RCAu2LntM0bjGdecvWzIimbhFej6mHufi68uia9ruLHSv2UbKfuvJeOqLTq929ZtDudG0izniN5dIGq2zunugx76vmUd9jjPFqp1T9yyrbwfhU5wLlW0WyHV37Uj0pJmHMOrMrzr9w5F/KN4Mb6yx3JqMbc12e2VipuZv6Wecz897DTm8TTx2btpKuU7ydEqUSTteZxtWdJqcq6USKqZy7joQb1HR4qYhWWHOuumgdlIMTUs+rk+yUquZ1K6+cDIk1VlnvsY1Wy65gYRXaDipwbkFlWtePml6RAifXv4egOPjbsWpnUr7kjBWc/lcke6PXWL7udknOPUkmd62zOWN4u6LOvNMxakuS46g2Q51O1XOOSKkyO2cvOcodkh2ZXe/qJtyjmXqtdi6xf5ysnc9SYP/Rsdfr6d6n9qw46PfAvWzUveU6ONiBzsEr+klBe5V2NXR1Jn3h0tTvtLjubIqzQc5OsR8Kpf8YJ+i32gLXfj1mYJ/ImrpWsq0Q8bs6xcU8SY5O//EeKSFZbXM7xlTujrTq6qPPQrJJ/Y4xxqt/Wa3dZiybf36h15JMt8CVm86l+9XNW71/qZ87uVbkmcng5OnqTWl29JMbI5TDHT8KW8RTFwykjuEqLq+ngMClS0q4c/oYBHTsv1PI2g4aBKdICXXkVTHXOTcR6Mg7VlgDiUqjxoDBQSKheM4FFgkcDzMSiX2kxmQl/0qZ7hz7zinr2rJKx79+a2CXHhtMBlDvWWe87uXQHxx8ZqzamUTM6Jyu33TGnM1Jiw1ah7NnmpaLF53zr3VVPkc8dXqGDpQuQnz//v3Vjqenp9cvXGff6I4p3cKfFkzGGJGIT/ptZleczVpxvhLp1EHzuN2sM308cxi1rDFePz5a99fZFbVBXKDpApYk06pDPoP2h/b7Lpx9/hvs3nu28W/oz0dgZ8EjxSC0IWO8fVIj2YIuPkl+rLNvak+c7VF5kk2qOsbwdkaPayetvvKDZdVLratcTaO2hzqc+i6N7SuB/xhvYx4SYrPyV/QXYy2SOFWe9it/a38pdJzV77LbSjxVmXofinxie5+ent7Yf+4+Y1/MSLkZOtvF64yrKIdLX+cc4dSRes6WM12K7Trfzcnm2rbiZ6SyUxvIM6gsO/dvxadzWCaekkKeNXylvBXlWnk6Jc3rJJ3I/HZkVGdE+JttdwO66q5/Hqg8pRyocJzTTQWlgzM9n1pw22DdsQ5MtxPIEWUKpu2UEc9r/+4iTXCOT+fs0+kvpat52BdctUhknduBMIZXKCsBAtN2WCnnSl9fLedvd4bv1dd/MlbsTKVTu0A93p1T+5DKqd9dcEAH39kMlsvyxhg/38/gbJYz7Inwpw2pvPW4GPWY2xmqNkVtQF1zBBeRiHmH1JbkZDJP2n21gkpX5BxJNLWF/Cjoq/Bev7z8+otrva52iO1h+52tcTaabev60aVLbXM+g6u3Q/I9dstZresjlbNDiH6muj4zVoh9otPN+jvZEbU7yUZ152fXujxdWUynfTTG2/fqkXhS+6LfqkPH+EV6kOypPlP9VnUzLqJOY/4rUHmcnnJ6c9XuMD7QGIKP3Wt/MqZL9qfKdLEKbU2NvypXH73TxSb9Vj/F7YDSds3ivaqXcut19vGsrG7+uvnNOmb23MmWkGSZ5b/FpqbynCxdX3e/742tHU8U2hEFqWE8T8VNZ6/K18nj8syUKFcAqlw1AjNFrZOVCty1VUEiqeqnA18TTNO7vDoR1RjQQDjHjgpQlRHr5PkZccU204FmWzplsgIqPudoJwOiylj7joq1fmsax/o7heralowG8bcTNAd/N2Z2JpFTzOsc9TpXaVS/p99VJz9ahtafbISzcVyZJqnlnAV1yOu7PnyHoDqJel3T6fseusfKaG+Ss5T0odPvM53p0lN3XyWf0gpulVtl6btMnH1Ocqd7rtfcS15T4OUCD7Z/ZfEikVIdVuz+CpytPvbu4HeAdkQXA2ZBm9PxdU3jB7f47UgoF+OsEEfOdqQ/R3JyOxnYnjHGK7KChJPucCIhVWVoPKA7camD3IIGH+tTpHhiJZZIulTv8Yx0UnIkpavdXGzrly9fXsUXZdfqHPuZx7tQu6nlaazjvlOMN4Z/RyFjpfSuIP52dmU3TtzpF1eO9pGb8ytlMq6+BV05q75Td/9mhOYMV2339jueduEc0kQiKbp/ctAyEluflLJ+ZqvVJKpKziQTHSl3U2r1uT504JmfSluVs6bRMlz9VZ+W4RSAyqbldoaAjqSTNU2eW5xYp9S03K4uHQNqBN3WUm2vruZUGi1vhuT07wQB98Qt/f+R6zr4s0Hnuc7xutoInaOO8HEkVefId2SVypQWQ+pYZdGdT53tK9Bp0C329ehw7bLVVeiqr85VMKDOcAUJVY/qdNbr9JcLBmbzf0bqOyfVkUerupg6no971ItXXd6OEOraV/Xp490KtcO6SJXqcrYnBWLOX9A6V3X0ri6/hxP+2bHar+9V18Ev0D7M4ILSTue7c9TxzgalvDxX55+fn1vbp+lpa1IcpHB66Nu3b69849KrJJ6on3U3zRjjTXpNm2KapId25pHWx5iCepN94eyRgz4h4R6zKzvb7XJKZAHtQWcjdBwUysbpe56+f/8+np+ffy5KaTu0TJJZjGdJPqlt4/hyfazXKr+is2WzclLcRblmddJH0XOzcTjT013+FRusc0Tb68aZ/l7FVcLqZuJp11AyUNBJ4G4w0+q1GWGU6nHKeKb8STx15JkjgRRKbJDkeXl5Gc/Pz68cT2XMayKXQnABgCOWqKR00O467Oqo8365tAWXJyn0Xew4zGS1E4FV6br26jU670mpOsXF6zPcy4ntlP0O7iXzn4579PXfCOr+ZAsKzs44fc/r/J3sBNORyBojk1vuU4/bzRZMqm0K1ev62IMSS2VDSlfp42XavvTIcNXD8uo875XqleQ0dmnorDpZqF9pa2ags6XvKFF97tpY7e8CAj2ndofjNNkiZw+SXemcYb2WbE3SzS6vq2MH9ypnVsd7lHNs2ueF6gvak0LSXUSyC9Tbai9oP5zd6WwOr+tiPT9jvLVHmt7ZL20356iLXep9QYl44p/11Pn6rTGNuwcp1kn3YxVOJxe6Jz2cjCxTv5Xcoo1QmbnDVtOq3XE7obqFeLVBXLzStlb6WhzR+mus6OIU5XQ2j33kfDhuqKD8qc+drVvxGXjs7onrxwSt29lulrtaR+ePOdvJ9G5M8BwJpFW9d9X+LRNPToDVDtqBKtMCHW1eZ/1JMTsFnkgoZwi0bhoEyljoVkt1sOux7rBJE5BBAhlMTlAtk8q0ZHerzG7iOAeZk6BTysxzL+dtNiY4OdmHjoDiNdd2luPaxfbes90HB38KVuzM7BoXCVYc91Su+161IVzEWCWgyv4kW6OPZ6k+L12uBFERTG43E/vPOYOl26pMfVdR1cV3RXW2IOlIpw/TgoCmn9mZBLYnOXGanmno9FOOZPfYr/xd6ep+0d44hzn1icqxet3JvBLszXCvcg4O7okUJM7S76RbXexI+Z2eTrZp1QbVOS56dHIpAUFCRBfMK58jZMrGFcpWsUy9XrJ05EoiHDqk9EkPaj0z/eVsSiJoXBqe02+ST05W3jtd+ND7w8UP/e4Wf9Se8N5omSqPI0j4PevfZPtXfEcSQwlVB/trlj7NWaZLZaTfqR08JrpxlOTYtctX7Pgy8cSBqURLIoac40zFWGV2aVJZSZGWMq2yOXBSWXXMlYM6Zt4xXv9zkkNyjJ3ycUpJV181zRivyaaqi4GIUwxkod02Uw5qnVhqfOgQd0pDmXKtg4pX4QxXAq+VHHxJXnI06r46UqkUNB+zW1E2rOPp6SkSVTpOXQBzq7O+mv9eQcFnCC6cMbyljFvKOfgP1NV1rKAeH+P1I9pVDvW/Xtc0WubMfrnH75zdc6vJjmByq9HJPrEf1FZQl2set5tJnX599wb7pnSTvgOP5I2WWXI5W7DqeOk9X9nBlGxTgutHPvrmbPYYrx+Zd9dVJtqb5OjrmGVQomUlkkptu9NpV3TxI+zALWW+t/16b6zI7fyWz9rez4Ck9xkfPD3996gb9QrvF21TKpO6Pz1+nWIWtS3OHiXbw9iO8jPGGOPXThz1leuF1dVW9wh3la27d+gHq75U/Zz8/RkY03TgqzdW/DoXXzAO1Niuk93ZlM5+Uv/X/VXbpnVr++qeqe3UY/4brotFFVxcYV72C9vm9JzrrzQO3L1izOXqSXlSHYy/HZz9W40ZXJu7elP8uGuDu3651d5cftQuOcTO0eI5KlWSR0nZjvH6MTn+piJlXZpela22ySnpurEMKEiepck0hl8pSI/a6UcDBk5k5tdHKbSMql/rcY9J8Lr2m6Z1ZFPB7RhSOfjXnpqP44b94sqlbEyj932M8Yr4UiXMQMUFarwfKqtz+Ckr+4tKWcdZ1ce0WuYKZkGHu7+rSiXJ0OVflbvDezjZrl/uUdZBD+0r1c+qz1WXcsHAvSjVEUHOyXd2qM67v6emTXNlu7odycT3PPFf7rQMQm0FnVq1G0qa63l9x10RUJWmrnMbPG2A6umnp7fv7+j0T2c367o6vSof86q+dKQVgz4tp35rXjqwarf5SISTnah7pH1Nm0K7oy971SBMbUXl4X1OduMWncZ2rdqQR+rt36Vj71nvbv9o3Xq8QtIerIG619kF2oj6Xfm78hzhk64lW+ViGmcDSTY526axF+ObAmOU0p9lJ9Q+FNh3VSYXutUnro/bUcqFW8Y6hNOBKeaiH88Av9uF6+pw42BFP67643Wv1PbXfS2oXSBhUmNB3+tUC0qFslG1q03fSVX9kTYVsF9Yf7UxETG7vjjrTKBt13z0K1hmij3vGTdQztQ/s3pd3g6p3CTbFWz/q11yDgmnvJwydg68pu/q7MrWslRZu/LcdcqldTojpDKwjSRmnCJzJBEVLleXO0WYymSQwhUEVVxaltank5PHjrDRfnBlaN9pW1J9ek7r07LSFtE0kVT2lI7jlmRVypcUh6ZfMS6zdLsK5uDgs4D62BEImnZGKnXvw6Az7urpbJSzPWMMSyLpMYOCelEs02n9ScfrCrQSS9STJIgKamPKKdW26CKH5qE83QJGHbtryca4ccGyaB9J1rmyU92sl7uJ2ecdkvNYMpbd1XRl8/UfhlyfOgd5ZnfoMB/bcfC3YdU/THmdbWFssPquvxSDONuXiCW9pjK6cnTBo0iGtChToK2pskh8u3yql/iPrdwNRZ1d9ek7+CqftqnTecTMnqS0LuZwNiW1X8tzdty1ezU+cHZAx6Gz97xe46FsTrW5oDaJCzQkwDrb3537SOh0QrqvlS+RiaksrY9+zK5s98DuvVmV59I7nhITzsGu5x0xxMAgKVyXhmXpdT2XlHYikJxcKjtXC1QOR6wV3CCqSVokUSnf+ncBdUZ1G6sqV17Xra6qsMopJiFVxkLTVNlOZjUw1S4qFyrmHcOgznXqSzV0PK/3gjIq20+CzMmvafRYlS77hn3q7r/Kyj6mAXOBiuuPpLQOPg7OPdkD9SqvceFBdbIjkVR/k4RKznxdd+dVhmRTHJGVdjjxN4kqvhuDUL2ppFPpPd25pPqMek0fp6ty9JqWT8KFu6Wcjis44ortcQ593WO3IOLskRs7rj5FRyKpzXar8V29ej/pB+g90TYwqNOV6/owr2sv26DXDwl18LfB+X6FZHNSrEE74GyN6nWedzaDMYZboEik1hj+zy2enn49Dqiy6L/hubiI0PigdI/+e+oY49VixcvLfy8f17aoPap0lafsleZXMqNkqLrdNZU1nedYqGO36MDxkWIQppvZbHdcv3cXNlQ2JZCUIBpjvLLrlVdjQ7fgofVUn6q/4Qg5EjNs6++0Mys+wiwtx1VnO9Oik0vXyXYPrJR/hXRalXt7x5N+O8dfj0naJEddFVxSfC4YSGU4Rd6tOqTAgccMAG4hnjgxVYnpX1iqUlVnXyd/pWMarUMd3TpHJ50Kwx2nNNoWEmPMp79T4OHeG6Jl0HHvdjjVfeHKv3P46XCUMVSZdOeZ3jcNiFQmhfaj9r27p04BJYLJ9e/BwWcF9S71rc4d1cel/zsHuiOZ3G9nH5IdSOm692nM8rDcAnWnOo3UbS8vr991VyRSBQDqrGqfjTF+Bgglly6UaF0lUyLgqdtUX7I99ds5P3rfqYMrjzra1ItqM3fBftbfdZ2y1v2jjXF2RPuHO4+17WkhiXJ25J4LxpwNOvhcOLb/GlwsQ/+Y+pivAmGck2Ib6nnaL7Uvu7GLlpPKIumkixxqQ7X9hJIYjgTSflXdp32h9kjTlg6qWMjFEarfSDqxfo13eL/T7zHGKyKMut7FKDV+tMzZfFTfn+c7W+PqUPtCO6jfKrPafpJOtQmCfc4+42N9KleKYdLv98KsXjdWUh53fYVcm82tWT7Gg6tYSb9yX9hHq3Lc9K92BVW8TphEIjllzAmUgoCZQu+CBsrQKfSV69rm1E/JIeb2+goQGECwHFW2iUmlQ6w7gFJeNSb1mw6xpnHBjxoKJWBYTpo8JLZckKJIhI86+1TOzpngykDld8EBV2NUDj2XgptOYTjCyeUl3P08OPhscME2yRB1rvUc0zk7wbJdsKDlzcinjlAigZRsXp1beU9H5xCpXSkdpbtk2C9abuk/rbfyaVl0+NUp1jbqamnnXPN+OHTOq+p8bVvSsclBWnWcGITQ1nIXVJWt9miM8YoIrGvpXwc1jTr5BdoZtbnsn3Qvjv04+NvAeZZiC9Xh3LlU6bqds9T1aoucLnakU/JjqbeS7Uk7rpxcKp/KqVD9qn6/szXuTyvS7k7dNVXn9P4wNlFbpOddnOKI95ltYUzDNlce6mV3b1y9Trb67Ygmhdpm1qn30C2sl/yaptpQhB9jmyqH7x109qazvx+dfFrxA1w5Lu6+Up6WyzHb+Ta/C6u+U2H75eId2eLIJBWKzn1y+N1KbwoI6Iw7ZZwIIyrZXaORFAuDmzHGKyU7hidpOGBL6bktp9VWMvl6zjnFVXal46qqpnNbSV39bJ/K7oyAjhmWo2BbamyQddfyaJhJPpVcGlRpO/kuk0RkMZ+Od5XNOf2UWfuIRmg2mdl3qS8Pfj/OfVmHc1aoM5w+p07ufnd2JtkAZ0dUHkc6pUf9mEflW7FD2hd0dJQoorOiDmd6sfgYrx+hqHorKNAy9R/eGEzo+UR4JHvpxoGSW1pO2iHb6U/6L06m2a4otm/FkVX7QYde9b8STW6hQ9ta5ei95U4wJ7vKdPTTwd+MjnQqnV2PqTHG0TRVVqfDlVhSW6TXU37Nx1jGLYRU+o5sSjGNLuwwxtB+o97XmKb+KU1J9aenpze2p/LVTlpN53a30hZozEHig3kLjIuIVD5tmfPDnZ1m2e68yjKzYZqfsQbTuAULvVdqr+sl4qmdT0//7Uarl5GP8XohRdvhYjXns/wuuJiMx3oujZku7lb/bkce9o2z03pP7okVn4C+9Sq2iCd1tPVYhXB5OkEdqUSFmK5reZ3idESTKvSuPleuS6dt7fqByrHSO3KIE59/4axpqi26asoJUNd1lVqVcj1GofcqEUaqPLTs1P40HlbyJFBJUAYqZBpOHQdKKHHVmYZVAzFVtDNSi/3nlDkJJ6dkCp3Rc3kPDj4bki51NkDzlKPF4KAjclTXM68jmVw9tC2unkRQ0SZ1pJcz9rQL2hfURVyZLr1HG88dT/oeDyWh1E4wTZ3Xd3x0CyNVd+Vz44GOFn93xNHKNe2Dggtcdu1WIoT4Lg6tj4FX5VNZGcTpbukueHBk4L0d2IP3xbl/e3C2Q+c/yaAioJzeZryRFh7UbqR3MblPSt/Zio7E0vPJBjo76/xSpqNecX2sOm6Mt49+lTyOUNJFEfc0R/3WGEVtTSKinB9+ZTw5vySlq7p5TYmLbhGEdbnrLJ+7xdW+aIxYclQZGvPUddp2Qu+PguX/LqyST65t2gYuQKX2sg4X/8+Ob+mve/V1GuszLBNPjkDS85xESRHxd3LmXSPctfRby+vk6T508tl2nu+IJzKfqgg1DYMHt42UO6TIKutAdvlcGqcwHGmlbaXinpFPzsl1CjUp/hX2lfl53d1LBmnqvNd1klO6C2qMNQLNjUEXDKS+oswu/8HBnwxHAClqXtFp7kin7pEJrVPrc3OZaZ09qRVz5/gnEmyVeBrjrWPCxw/G+O/PK/T9TmP82lqvO564+1PLrI++vFzJ/CpHbVytuDq75pyvVdAJXgXHjpaV0q/spEpgPzJgokzVj1zt1z7XsvR9JM7OuH6i3Th25OBvhfMNVRfPCB7dDVV5yraM8doGrZJHLlbqyKPOpuh5bZ8jn/iOJ2cHElTXlJ5y6Wlv1LYouaEkiNqaah8X21XnkWhyRAdjo25MsJ0pLY+TvU7l36KDZ7bJ3Ve136mMuj/8LhT5VJsY2J40Dn63vbm1fsbsaTHNYXavSGCluPhWfyTVvVLuql5QbL9cPA1IhSN8khKbOXEpPRVocsqd8++UumtjJ6vmobPo+sQp4pq0aRupY005GJzSdCy//jUm07A+l0ZXFtSRpTxKxLg+cA4wyRvXf8zP/tJVEpJAJJbUaU/KodJxBcY9HqGBVcmlY6IzaiqjluX6ygUFLs/vwO82HsStfZFWGW4t66BHcpTpGNN5Lmc5rdyqI03SSW1ClcnfheSgqywuYEiBhEtP+8l26nWC+kL1mNPvCrerVXc1FblB0qnKJimjQUH1E3faqhzOljk9WnWskPN8BE/blvK5cthflCFB03WoscRAjI/Z6a6ocvBJ+nEMuHaxPV2f3ALX97eU8SchzcfUT914V1zt578N1FV6LhE7lZY2yS1WuM/sReEqg7MPiWRK74Ki7Un5qTuczarzM6i+LL1fOkpjEOopfd9d2Yoxxs9H9RjU8/E+6kFHAmgaPa9xw8yu8HcXQ7i+7cpLZbjfqcy6Vva6q7P6msRQ3XtnIzR/5eG7oTTe5E5rlrWi01Is7I5XoHWyfnfPEqnk4nQXS6cnblblTmlXyrlqP1ftyI5uKFze8dRdd4FCDU79rX/hWfno/DM/FWfaprryUtdO6brHNJzyd0rFKYY08RTqJFNBVn1uhViVjE4Ut+2vyCgNFqoMEiZU8nWPVOEnRV5tTYrFTVj2hwtmKq063VqX1q2KU4On+q0ykVTiS1/1nrk21r8/uMcoKo/KpIaV94HgONCy70mQPAI7ymiMtTasOj+7itMZtx3stvXgLaiX+SiDpnG6X3cU0WaoXXA6nvrd2Sat261gJztT5Tuo3klpUgBPu8ydmPX4NAkkXSWuMpVcV5tSulYfo+NOHCWjtBx911SRKPo32nyETIkU6jbqaGdLnT5MxFP3Im8tQ3U1/RwNpr59+/amfq7cq62rMag2yt1Pvm+j+rquu4Untkfts9qiSq/3QMHFKvanqzf9noH9/qfBjdFZ+p2A4tifPTAmqXNlR9ROVKxS9mUMH+foeRe7cBHEpXMvKU/xjLM1HcGlMiawPdpfzhcv3VxpVDc9P/8XYv7v//7vm9iN8YrqNtX1ZXtUT+ouqNJd+m6oKpOLFZqHOlH99MrvdBpjGqc3qXtTjKjHWh9jK5bnSCG2VT9qX8cYP1/b4vwcjWU0Dqp78u+//76Srd7zpHmfnp5+7oAqWfmqmJJVbT77WvvFpVlB6nfWw37W604WytghxYWpzlk73Dm9J6v5ee2KDd61a2NceLl4qpTHquTSdZ5junSj9LhL25XrPnVtRrIlsC+csukGhQ4axxIn8kaVIBWWS6OkDeUjsVWKWh+T0NUHlY8rBiTKVG4aHMqiZWjZs/uritmt8lNxU+kyOKs8Kocb8ySqdLVB+979TudYR/WLu/5IJXFw8Luhc3Sm8zu9m/SGW6Dg+eTgOxvCekhuUe+s2K/CDqnq6qwPV587mVyddb50XulGPqJHfap5nR3hNbURtEPcdeqOkx/Ce8Lr7G86dhoQuTayrc7O8DzBvuX4qBVmlbXstvNBKo3DzM6wPbvXruIRZR4ccL45He4WMRKx8/T0ejG9I4xSXmdjdj7qq7r5v2M3ElbnvurDks2R5Eq+l1/OhRGNOapcJfqrnUpc6aPHqrsd+aRyqX4uGTubQJ/a+QHOrnf3pmRirEWyyPkZrt+IZPuq/3QnN1FkosaR+pLxulZjsrPBDikWrOMdrI79dH9XynexXJXBcxwbszp2bF/iB/TaPeb/Pcq4RDzRKSWT7jqXinfn+hh+x5EzEq4sV08hTeKVQUGkwaXnVBGoUhzDD1Sm1YlMRfz09GsVl467KjJVvPq+CF1JUMJJnWTHPmubXMBQ5x0Jpdc7coYr41ytVcJnjNcr/+wjNya0LVWOGiW9f46cc//owF1Z2lZ1ejpybqbUmCYpaJfuOPMHHw0kepJe767RFl21IY5k4so1y0yr0jNZSwb9LlA31rmnp7e7VOk063nNS4dQ7UK11+3I1F1L6uDqLig+jke7onaKxA2JKEc+sV1uMUDTUe9pm9k/7h5wVxGd0rR6XOVyZ5mTRe+LQm2X1k3b+vT061+G6ljbnvpg1c7UOWfL3PWUZhV/o73qdMFqIHSwDsYDNS/4EnHqfpI8ZQN0l9QY442NcGQVbYojktL1ZKdWYyBnZ8Z4uzC9O/ZYl5ZXv9UXVx+dsYraE5VPd+G62EV9ctox7pbSclV+ElKprYx12A91zHNjvI6XnQ6mTuA7FbVe1uH0NdOxHrX1apd1bNE2ln3TR+sYd1Eu7buZ/dYxeEUHurY7O8VjxrQr5c/SrMrPezezfc7fcbZ/B7uy7tybS8QTHTcqtLqWFB+vaxmqtHXQ13HldcrWKeKZ8mXdtxj3pFzcbzeo0uDRCar5VKEqYUKyaYzXj72pstSAg9srO/KIyklJLc2r7SBB5VbAFZzwbnCrDKqYK50qaraJJJXK4IyQ9qMqVJJeBf7V6MvLy6vdAFSmnMCpvZWWYyYp7K6PDw4+Eqjjx3gdIHR2RvU+83WOunP0V9I6GzNL50inzu5QH9BRUzswg8qo+ky3x5ee1nKVeNGAgM77t2/fXgUESjzpN/WntkPLYxq2RfWn09ualnkLK34A+8x9OyJP7xXldO8D5KKKO6f2lmnGGJFwUnvL9nV2hli1Ic727OLYq4NHIsUljvAh+VOP3ympxPhFy2J9s7jFvVYkpXE2Revj8Rj9Y7Hqz6tepX/LvCxD+4J/fMCP6iDdtZRiGhJI+luJKsY32ge0P86nrmv1rfHDLIZL/e/8E+ZNRBZ3Fml6+gUcbyyTPhTrrePaVatlF3TXGRf7nfxqxwo6zjTtCik1g5PZ/U5lzupydk3HSWdHd9rF8dbJqvV3ae6FKzb6pkftOHGcQnUTgUqaJFQimlxenVjuuJNjluZqn/A4DU495oR3INnBAa5KfIy3L7/W/lFlXHVSJgYCVLiquFWZVnlatnOEmdYpB6ancmYbxxhviDM1YlWuKjtHQDE9DRnvI41ypdXnpcfoX0DO+z9TMKmv9Hc67/LcgpX58pmDiHso6nsq+z8ZalPSo2wzMofX00IE087exZHIpjFG/Me6ZHsc6GBTD2k+JYlSWQX2nZI7Wk7VW/98N8brRYbSU7QNJWvthOK5+qaNSXpWbQbJJ9WT1GX6DgtN65DuD3U5STb2Y42Xqp/BWmf7ONZph2fvMeQqs/pMWicXhpxdIFZ8Eo7HOneVdHove/WRQb/xb2jz7wB1uB4roTTbkUTf1xE0es75ubNYZWfBItm5BEdCuV2azg7RH6furjmrukf7gzqRi7luIdsRTyWz6jn9Z1UXX6jMrh/G8P/Eqr41+9ARKPrtyEb2pSMt6rh2tOqTLar7K53+CUX9ZvsTYan+QV2r9zrVY3a81ySd2LfaH/QBXHvHeE12ctPFDtx8c2W5/lY4GV35Lhbv8iR939nRJPcY+dU8TtYZ0jxnml0btU08OWe/zlNYKk29nhQqlXp6+R8nriuL59VA8DrLZroroHLq4PonEQljvFYkNfGVaOgUP1e3SUKxTp30uhpRddNBrbRKsrjBqW1Kg9ftRtI+UgOlY5CBG8dqtZ1paQhV/jpfYNmUUZV/GQuVg3mdAqu8KhPloCKdKYYriuIe+F313oqr8/9gH06f6w4k57TNrnF+0SY4O+HsEh+rI2ng6lR71sHp0dLVpZfo+DvdNgPtSPW52ymk10kwqdM/xmu7oDuequ16rLpSdSJ38bjdQE5GbVPJ4/Jpe7Qf3RhxAUEd085W/+iuVu0H9q/aGd47tYd6Tu+D9gF3O1XbKmCqPM6ncfW6Y+1jrTs5tleJIpXxI9ir3wn6g2McAureSLaDepukk5s/Sf86XcW6nS1Lu24rTfdIN0EiQP3ZtEulFgoK1NsJ3TXqZEfKJ/2otqbaoC+rJuFUbax20P6rvGpn3L/AFRjPODjdpW13fg37VYkYjjFthy48aL8WGH84skXl0T5R2+baV3WXveML4Nk/Gms528Nx4GzWLmbz0/lCCvoUq/W4NuyQQDMbmsYxx89qfR0c4eT4BZe+wxbxlJznjvwp4fR6nVO4tPq7q4ekl1PEyclMg/rKTRrj9WB1jtwKSOrUsX5XHVQCmkd32nD1WVcK0gRMwZCm14CIDrkjoAosQ8+5vuBqg/aRpnHpueLh7j3L4G4uJc+enp5eraawvgJXb/jvDW4bqfaj3ge9ruc7xcS8ziDe05mdjfHP7Djfq21X9crfAur3OqfXdvS7WyDRa1qP/k7kkwtItJ6O9EpjhHqeCwTcEZTa7fpy1tf1ncgQ6vOSryOe6lgfey49TUKnbJKuUKsMGkBo/ZqGjp5+O12ubWb/caFLy9X7U2nrnLanHodQ20Q50v1J/hHrqLL0sQvaFvdvUGzbqp2p886GaB8lW7Sr+9/DXn1EdHP2b+mD9wJ1aNLtmqbSFRh7MK1eczaiI7iSPerOKZz/Th+9ZHMLDySfkp3Z6esqV3UodbbK6XQWiSbaJCWbdOerk13rKbhdt6qPHIHmFjicnu3uocrkbJ3uAGMbqhz1H+oRR7Wdzqdy9TOmK/z7778/7Ur1gS5yuDgr9QX7ysWDXTkr6OpNsS/niZ5fqYdt2pHVjbfdNpMruFJGVx79VedDzHC3f7VzzvAsHX87R19/r+bVa52jleS8Osid41vl7AxAJVX47SaCc8qrXl3JcA46CRXNy+CCBkHLcls5aeCSgu5QZWheNaR6/8d4u4tI07tvPXaBVyEZG+bXcyoz0/Mc25Qcea2L1136W8bhwcF7g3NTA/ek/xmsu/mf7EFdT2VUvfqSWQYJyV45mcd4u2LkdltynioBw3OpXQ5abukftR9Mo6QF61QiXRc5tNyyC0l+DRCqXDrIatf4rfeM7VTboaDPoMf6rWXp58uXL+Pbt2+viCd1/uujCyHJB5nJle7DGL+IL32vk95D7dM0J3btzCwt23IL4XS1jM+O5JP8bf3waDidPUYfh2hePXa2SM85osG9sLz7VB4th7IqkTOGjxlIaDhbU6jy+e5U54MTXeDLP+NxsU5n19S2lMz66e4XZXR+QvVT7apiORqT6H2f7YbimKD/UKi4q8piXxX5w1d3qM0puV1sVn2o41v7RMvQPnDfdd2NWfabk2PFptAv2IEbBx3ppMcrereTaVdvr+r8rk6X5woJ5fTGPUinMS4QTzqAqGT1eoHOHa/N8nf189gFKclAsJ4rA9ohDdaV8tkHdPxcGRwIGsDodRojLTMpJpVBy6JiYlvdINUx4FZXVC79vZqX/VbjQeVVEs0RQe68GqH6zZX8MX4ZCsrIevU8iS5HOjlF6dApgFVl5tJfxWdxkq8o4atp7tGvfwNmDksXCNCJ6nS9syFahjr7zkmk06a/ndy8/5yTqme5Y3OM1++uo6M6Qxrn1Fd0+FVfqn7jqjBJJtWRda2+1SF2UBk0kFAdyF201JNJf2p/1O/uEUqVSRdblHSbBYtVX1p44bhzY7bqJUGpwYfW4XwJVyf7NWFHd92i5z6SDr1nPWnu37P+1UDp4C2o1/Vc5xtrOuZJ9oR6hgsZblFD7RBtUtXhCAT9rmPqRfc0gercapcuLOyOMdeHJPdVPr3O/lWZlaBhv+u32hrnCz89Pf18VFnLZT8oNA5i3630hcpLokbvQWq7Ix415qk8KlMiRp29c/elPrqjVn0D2r4ay9x5Nesf54ekebjS12yXO06xbH27+rvy2R6ii9MSuj64ai9maVbKXrmvii3iSRVgp0iSIuZvTp5SpqyvrrkX/n358uXNS11VQRecIt9x3LVT3c4VTcM2knTQb/ZvOpcc0fpOE4erAvqdyAgXEJXyVkVTCrr6459//vmZXre4kvDR4KVrO/tBFX390wLlL6VIElINCf8KXPuwoPdY5a/xV2Vp8KF5S9myfXpex6DWxccmuSOi2kW5VcYqi3AB2CzNLnYdbMVKvfd2rG9pa2E1kLhHXX8DqJ9rjvGl387Z0X7Wb0cc0R7UvxWVTWEdfNEsAwIXNKjuU+dR21c66fn5+dV11XtPT08/t7rTOas21idtedc+mT2GQLumuk71TtkFfdG16tkim+q4+vL79+8/9Xh9K6ml90n1bfURZdEggTo0zTuOCZKM6V2K6vxz55Pb6aT2iIEBx4m2u/KpzdZjllN1UKZ6D0ed00BA77HKzOOEFd13i97TvDvO8gwp8H0EVvuoa1sKwJLvdtCD843Ej+p2ZwsqL+cLbcrsOt/144iqeql0erdTgbtgOR5Kf7gdMozrSqeneMb5mWkMu3HJsqizVb9XGfVRH1w/qtt0kYMyVf/qH2GUXdE/1VBbwwXkkokyMq5Mvkrdx7q31HOVpggxtYl6Xd+pqH3t4isdj04uhcZ22v8cO/XCcR1XWn7d2zH8YhN1r4tnqatdrKvtdL81D22oK6vb5DDjDlie9uFqni4fz3ftqPROXyQZODddP95iK29+1G4Hq8aQQYO7rpNmltdNNpblUBNdv51cDCK6Mnj+KtwEc5NVB2gpWlVeqrRZnpbpDIYL8LR+DuJ0foy8RdU5VWO8fb8GyRl91EOdfad0Xdv1fDdGOM64TVmVNNNrXRoEqpyrDuuqElgpd6fug4NHQeddZxN2dPqsLpajzp37e211IFcMu8LZE65MUqewH7Rud30XK44b7czT0+sVZX2MopxO6jfnSOmjd2o3VXcn51T1rPbBzEF1fgEdZtpQDXK0bxic6uMQlcbJofeyvtN9rX5INnWM/xZlku1yY9QFpx06e7Njiw4OfieSzt/R407fOj3CMlVPlJ5R29I9gufKWwmEOb9V19JXdm1j3a7s1D8dlFx3utvFIFUviSfGEZVv9i+njGl0IUTLcTbQ6WOCj0jyHtcYoH3T45eXX//aWvaOH0d8qfwcMzr2XJ9U2qpPSS7d9cR7wX+/c4/rUT62dTdGcXMw1cO6ujq68ly5j0Dn1+0QQZ2+YpmPxOV/tUtYUYbO2WNarkS6407Ba5m7zjiJEf2u6zzH7apO+TkHz8m0KmcKCgputaAYdCpNddpL6So5pX2rRJWeK7hHI/R6nedOKA52/d0RU2pcVMG7lQLdUURiSO9lydTVzefUtd/TagMJMu2nFAy48eLIO/d7dn43zcHBo6F6Xr+52ltpC7RN1P88Zl31W4/LQXx+fo5/re2cubTS7aD6RR9X00eo3HxXnUz7WbtPk35h/u4+FBLRoY5mXavdTbr7iQS7Bjvfvn17lab0Ockl7trV3VGl0xkkpEcf3M657t+r1Baqjfj333/fOOV1rH977Qg0jjeSV5qWAVj1ubt/KkPJxnc+cRzs2AeX/tiPg88Gzn9nB+p3in2o+/Ucy6eO4Xm1MXySw9ka1kus+NBqI7iIrvqGMZazw05nunIph/rjWhbtDcsunaw7l6qM9D6ssjEaw+jTC6qz9VjjIWcLV4gnlYu2RndX8d5pP728vLwhdmgbk213fhXHvpar/givadz1/PxsF2Ncn3bfHVL8Q7gxQnncb1eGi9E5Pru4NZ1zv1nXzG/V/r6VdLrFbnf9mHCXHU9J6e1epyLrPppXJxJ/d+XOQCdR63QDf0YGuPOrSIOfdbuJr8fVdmWpS2lrmXzkQeus/qUxYD8pqcO/Ii0jpDImpfLy8ovpT23k9k8leNRgaDBKI66yuZUT9r/2rZJtKr/KUn2iYzQZWv2drhMcZ6k8l7ZLc3DwXqBzPYa3D0SyD0zD9K5uFxCUo+gex1Bbk1aFFdRfaV5ylxDJee0X6jbusFSdm/rDXVtxUlSX13f1kT6erbaH96CO+fif6nenr6s+6mD2pbPXyfl2xBN1sZI++vhgfZTwUbvLclQmF2BqO11e2tf6OHumpBz7hON11ZFN6ZJ9uRUr5a2m+Yi4V18duz1H5ws6fangdafLkk7pyKf61EKHI55U/pmdWZ1/3cK5q8/1F/uS5akd0H5TdLtD2c/O9y+SQ8kO9hH/0OLff//9KafqWt0RVte0Ldp3nbzaR0oyOV/DETK6c1YJNm2b3ht9p1K6h2lsFkgkFCnG14zUtW/fvr0iw7jg5O6Di01XkXwR1lHHjqRZIUvSWF317VbTUBbWxfpW6lFwfs7kcnLcE9vveCp0LLumTw5t5wTT8XJKndeYhvV3Ha5InZ9uNCfMTAFpHucMpzwrfc36ueuGA5tbU0vxsYy6zsf0Kl0pF1WGSrSoQlWH+B6D2hn75HBre0hk6XUtV8t2fanOvPaDbputOrT92k8MCjSP3td0TJn0dzq/m+bg4HfAObeFZBs0X7I/mt593Mpg+k7EU4dVZ4H/JsTrOofrnDrdpYdXyXTXZ67O1BY6kWVn+C9ws/Lrn9p0R6m2V/WTtnWFCCk44onv62J7dRFDZZr5IOrvaH7nw3AMar8puVZjw5FbDBBIOql9TmSUfnd2RtvFvn4U+dRhN4j5KPiscn9mpPna+eTJL5/ZE12s0DlIXZPIAfqtlLULRhl0axnUHc4OlB4f4y1h5/K7sqoMze/60f1mmzu/tQiSam8iECtvtxtUdzeVraEd4m8X11E3Oz2vebRtupjBfDzHa7ooxDHW2TnehzQ2dVzr+NDzSlipbHoPtM1OjmRzEm6xOWzzCtJ869LtEEZ6rONshzxLbUmyP9IWLRNPTlnw+uxGJWXUpeNg7xSclkd5Zky7Q+dgUdlTqc6c60QkuPr0OCnRRJi4urVPiq3WMpysihrsml6fD6fScyu2StIkA+J+q/Kqsvm72lYKTwkz7S8GKjou9RoNvgYA2gfabpJ+LPPLl1+PnWiapHR5focomvXvapqDg0cjkbCF5Mzp3HZOXNKbLId5klOnOrQ+WtYq6DhQR6ldoX6mzFxEqHOpnW5+J9k7u0L94+pVpC35CkeGaKBQ19WB1SChI6AKyYlWpzyNwXKqGbCoY63ls730gZz/4lahy3ZquWl80nfSuaV2dNUBXlmM+Ah24yPIcBW7wcjBbZj1JW3CrAyddzr3eL0+7uXdTKekkyMpiBTcdvo+yeDkps/rdFjXnkQ+dX3qjlWGMV4vKpct4HuTdPeQ2pPSGXzPVYE7Ztxjix3x5GJY2hkXL7HvOrKp6mHspXFZtdfZiBmcXSMppn6Ka6vGmyRPSoZEfNDe1zmXnj5D5+8w7mG/s0yiI51uJaAcwepIpxW74cpakWGGK/bopkftOqXROfo7ZbvJx3RMQ6XmFGgnvyIN8DSI3XZSV06aMKkfxnj7snCF628noyPG0u6ckpFKQc/pp94N5SZDHZfi4HbQ5JjzN9Ppyq72gQYEukqtCpr1ULFrnzHooyOvwaGOL20nnX0aC+1zba8bK24caTs0DY9303xWJ/7gc6LTBQVHNO2gc2ydQ8hzNbdnNqcjQdw1JeKdM8qyqePqNwkR6ketqwsCUj+5shTq7NIeMG3SX1yc4OMgup1fdSGDBNow/U4kjfNhKAPHRiKy1D64trI8jkHXFtpfBgEkQ/WeaL11rOXpPUn2wfk3nS26lw1Z9dmu6ISDvw+cW/eAizeSHtXf1AWdjmE52g7Ow87+uLpn7dB03AXliJRKR3vm9OtK/9X5kt894ZB2PL28vPwkP/SPI1x5XBjWvlUC35ECBO8jH9uvvtOYRvvN2QbaK8YaVa8uVDgCTH8zTlLfgrIwntI69P2CRThVe9y/wLr7PiOK9J5ofndP3D1M/g5to8MthFJK48a5S+8+K33XyaTYtZ1XbO1l4okDrkuXnLhUZlKAbqIkxZmU1WonObLFOWUuPcmnmVN2Bd3g0jSsQwMbDugvX778ZKMrryoGTUvnd4zx6u9Idesl69O8VD6VnvXVd2ds+U9Kek3HTdWpCnUMv9ugQOPFIIoGiWRVUmasgzvAqCSpaKm43BhLxNJOmoOD94IG6gV1WArOTsyc5zFe794lgdV9EhlFW0QZ2TYe85xzhrV8OmqOiHCyOGdfz1OXuGM6rJWf96ZsgLaPv53j/vT0+h1QbnVXAwx9D0cRUbRjrg5tCx1y12YFnXC+xNs56bo7SuXRfnR/lc45oPZSbZkGX+7jHsdL9uhWG6JlHlty8JHB8ak6KelLp2O7eGQ17tC8OnfH8LpB5Xd+sR7rOfdkQ1o4YXu0rXqeu6CqHr2ueruuzxaQkp5ysmscwEe86rraqVoor7KdrVV9rTGL7rzRe+H6doy3/2pH3az1aj1cwNE/RlK9rscqK+9LIpx4P2ljEgFW9q/aWGmVBCMRpdc5fpy95v0pOPKoynL2VX+7++fmKm2fu+cOK3aPaZyuUajM9J06uV19jqxyxzOs6jZimXjaUZy3ltmVM3OcNE36PQOJIjfgNG2SeUZQJbh2UUGvDGySHyybA9EFECSXxnj9N6xECqYc8UQlyTIKjhFnXS54UcWmbV7tf/3tCJ5knLvxpsGSuz+pzhm69DMldKW+q7JcxSMDGOeoHfweOIefc9Ytdlwxfpo3OcDqLNJRcx+2pWtn9+1sXB2rrOyL6qu00MPHDGayun6nbCSt6lza5VP6r5xRJ68GQuxbDTCen59/vsBcnWbtT/dYBMt2HzfONBCoY22LW/mtfucqsfYvx5cGndUOddY1yNB6Z2OZbWewlQimHZ3+CP1/D3w0mWbyJJ127NV9QPJWz3cBHdHFMbuxh/tXTbezlvV2pG8indSvdv/OPIbXw25HTB07/Vb16zXqxZKTOsm11ZWtvjSJEO6w0f6of75O9TKftpFlKUHk+rHupVu0ogwkuZxtYn4leEpOtWFqU7j4xjFTaaqNWp/OG33hOWVxMtY9KXKQj43fAteGlEb7WK/pdT3/qHim4w9S+hQ7s22aj+Ww7llcvGJzdvTcXf7VrpAczRLK3Vgd+DoA6RAxjU4090JQLVvrJ8PbwaVJN4AyqxKsSTnGnNVMoIKmfG7CVP0qn5JGnKg0wqr4Sslo+zRoUEVMA6STRBl0/dZ6tZ1Vrxovl0ZlqTbWX3zWllkaTBpyPtKh53kvaEBdwKdllYFz942OgQY37McC208jqPXcS6GvIimxncBlN09XRofjuH9ckITSuT7GePUvKukfbHbgnDt3Lf3NdaVLbanryYlI6amzaqWWelxtm75wu9LVsa5EavmJBKJcdND1mG1Rf0BJmjr3zz///JSJjwcWoeTehae6eozx6r0e3FmkY0aRyEQSe+6+8H5W/9JWlUyq+1UeF3xoO50OT0FxXdNHHLQ9akt1tb9kVBlmu5Xr2iquzkliRV+7PlnFveTssGtzOl9kJc9BhvOZVK8XnI7XuUVd6uZxF4xzRxNl6sqmTnJxiNstofOf5da32hrqR9ZPkoP9lGwNd+W4sbtiXzW2GGPYRfSqo8iSf/7556eNUT2oBBHtCGMX909vHFf6L3YcC4wH6lvbwjzaf+6PJBwB6Gw+7V2ymSRjq44au1Wvzpuyye7JD83vXtie7DZ3dTldyvYonM/i4Pw1B84B1sNjyq3fbizoXGW8rGV2foP2ZSen1ul8D+alf+zKS1gmnlIwSaFvua7pnAN8D2M6a0faXtuVpWV2QfcjnIGuTOe0UtFXPzPIK8efAzhNKG6h1XpVgaRJ5ya3S1t1di/mTsqCylYnmKZnv9FAXh3nVY5bXVKDrSsqO+jy7JTnxs3BwXug05NOB70nnNOt11Ywc5q6+evSdX3FoETzdHYule1I/6THUhsYwGjA4MrQ9NzBWtdJbtV5F2SxnuSIJjKRfdONAbY3BVSpbrVNdAiZnrut3B9qOKeW9axgdo8/it34KHIcfA7Q39QnAJy+dHqBv28Zg6sxUNJTVXf3iA5lZhmu3U6f0tZwE0IKeFln6ufO7q/6q6o3lbBJwTcD/rQ7i2lTe5M9cDp9Zt+ZX88zDtM0Xd3VBrUnFYvwEfIkj9p2jdH4GF7l0cciHalOe0J/Zmd+XbVNvC9u7F8pM/k8/L0Sn6YxnPyTGQFVabSuVd90BZd3PKlScyvDKlCaGDuO+5XGlZy7TrtL50imWVpXfycPZUppXb+utCelVRKGaVQZuOBF09Q7nlR5VX/oygF3FlHhzIgpZ1SpUEksqTKlw+3GoesPVeg0OgRXHeq3lqvn3H1xRkL7xeEjOf4HB1fREQYzHTxG/hfTHZuj5aR8nf5QlJ5Lus05V2kuu+udTdF6WDdl5Hnd5q/ndCdV6kfVgaqHy+ln+ysNV8N1Bw8fD1G7QsKlbA3tD9uscnXBgJ5nn2s5rgzKVO13u62c3ndOpJZHO6f1a/DMclM7PzsSEXBwQJBgcT5vmh8MzBwhobqN+ZJ+WYmjOn8w2RVnBzqof8539CU4u53mo9NtfESM+Xd1lfafxgEVr9De1G6ol5eXV4+RqS3SnTwai6R4wNlAnnftcrra2VW1By7dGPkfCGmzGJ9V+5PtS3K6eklq1Y4n10/Vt/SDurHk+u+q7l8ZZzp+byW1Ovm1L9led09deS4GTbIUGJ9283YH28TTbmWpE1yaWbpbMCN8WLcjjCrNLlvoznWTZaWP08SfXRvjLYHirtV1VUqzIEl/J4NXbeM/vZHU4mRmv+tquQtu3CM6VY4qaQ1KxhhvVtUTAZX6brYV3t2npIRVXvYL+5h5aKi7vp3lu0V5HxzsQMlh5wBxHO/YIzf3aLz12OlRl17RydMFAp1jRZmdjnW2i7qC57Uc6kzK4oKn1Gann5yDzLZV/fXYgz7arWlcYKg2gI/tKQHl+paPINRjFq5vea7aU/asftNR10cKxvALJi4wcPfC9Sk/tIuuzZRB66HNcPW7PlmxM49GCkx4/Chf85FY6c/P2K7fAUfEdGOWAWBXZvfYFcvQ353tccedDKmtXSyjel5tb+lVfZddkkX7QH3w+nY7athWPd/5qzPQ5jA+cPpAfQvGLeyTMX49fcHHwJwcqufLTnR9WfaIso/xy26VrXS7ssbwj+c6e1H3pvI4Iov9x0cB6R+lmGg2vmk/eP/TGHe4xS7N7GLytVze1Xq0D10aN487v0HhFuBW65n5pTtYJp5mj6DpKt4KOmFdJ3JAX4UbKATbyrTuBrgBuiNPkk+vUxm4umf9qnBEh6ZNA7IUCmXiS7NJ/JQxGsM/8qeK0jnd3eTUVYtCIqS0fSwzyZWc75nCUKWu16i8aVDS43Y8lwy/G69pXDFNytNhV4kfHCSkseT0sjqFDsmZm6W5KmNK5wIcFwx0dVCfcc46G8x5rGVyld/p7cpLnTYLPgppdVZ1MuV9ecl/ec36dAXP6W33uLiC8jiyS8vjOc3HoI26Pr1M1Tnd7BN3rHmcXNq3tcuMfVA2WfuI8ifbQNlX7EyH1Tm1itXA5LNhpT+PrZ2D5Eidq++kl7vyZtixU1quCxadX+rmdrI1/O50AOt0srgyHMGTbI2ri/IlXdj1PfVh1UXboLaw+onntD7KqHYuxQnd4kSKeZzN5HuVNP5hHLVip/U64yAnq+b5+vXrq3+RnS2gJLjYi33R2SLm68bojl3ageufHcKm83VSHr2W5isxi6ddevf7ah9u7XhyykMbx0k06zTX8A5XG9mROSv56ljlSAOeZTsH3QX9O4NE60u/k3FgnlqtdfdNFYE+h5tWU5kmGXGOIydjGtg61rQN6boq67rmVj/YP50M7Bv2v9bJFQH2DSc8d3Kle+ccAPbj7Fx3fZb+4OARcIExx71+JyT9sGpDkh7unM/Kl5x8pw/1fNotmXYhUS6nw1wb9JhkN4MElZlOrEvPfM6R1HIUVWa1x/37Hstk+3m+ytR0rLPy6V9ed06Zs7v0f8pZ1wCBCwxaFusk3NhLUFujZFPy0WiPNG3n2Ke6P4LdSI6yHj8i8Dj4POCYcPqB6RQ7QSGv7469pLs6W+P0nrOf1PEr8qfzjJlK/1L3JNuh+iOlSbqe7ary9KO7qUnYuMVy+uVaH/u1ZE9PQ1AO/U7xHe2uyqllaXkll9vd4tqWkOKI2afSafquji6WmqHzcbqYZtVOpXnSpZ/VsdI2+q8zm8U518np7tN7467/areKFBTw2q2Y3Sw6651Cc4OYaZzSTXV25zt5NFBQdIqrO0/FoAx//Z5NuE4B63VXztPT05tnfLuAzrWXwVZ9+HiDM0IFNXJUhMkhcbIoqcXxp3Wm3U4pyHFOzkxxpr50/efK3JmLq8HGrMyPELQc/F7UlnF1Wp3zpHN2ZuBTkECHyREiqr9Y5yzAVSeagQEDhK4/3Kq8a1dqX2pTpXF6yPU7F1LSLtyVYC31NVdtmZ+OE/uQAUalT38ZPsZb0imNl5LL9VWdr8ceeK1zwrvdai641DRp7PPRB7a3Hg/UQMnd95mtWbUzMzxK97vg4bPiloDsoIfT7bvjRX3LleBupp/V/nEcU986m+LacqVtKb5YaUvVSaRHfVWfcDcm61ado99u0VfrrPuserCu6xiof85Vn0Tvh8qku29dX6mN6GyNyuz0KReJSi7uuuUx/XznP3G8dLGfi1EYX2mb9UPij23r7reii6e1rSwn5WMZrv3J53NzNKGbT66tPOfi7bq+Mq+Tf/Ne2Cae3E4Xh5XAmcdpFdSd3wEJAHeNAzE9bucUfrrZqjxVKXSBQ6pnB06J63mWTaWsSmxF+TCNk59ETuV1gWWa7EkWKmI6zzqGyijxr7srnQtqZii5VoKMTpmoXGo8nXLR7+6+dIqXaWdlHhz8DpSTxBdS1/HMsedxSu9slhJgHRHg5o/TX8zv9KKTKT0Cl+Zp0ml6nTqg2/FEBz/p9jH8btCZnJqX9XY6Wp1vyqo6WZ3aSseVY36SvBxTLJ+Od+n1GVIfuWCA46vr15LBOf4pUNEyaNOd7VixM78bH0mWg48BnUtpzLq5pnNcfzs95fRFHet5ylN1qu5IJL/qwGSnOv3h7OjLy0t8fYrzczv9yXhK60jlp6BaZXX3LNnRul/VV2pD3G6nan/Vx6cj6BO4WIhtoW3obA37lO3g7iftD4IbFjS+YJ+zDziuVI46nsnAXVmULb3flvOy66tCipd29X+KN105nU1ekTn13U57KZvTEUy/Kte98a47ntygm6V3v2eTNWGnw7v8HNArdeyev5rOpUlOYYE7fbhrhwNW+98FYLU6wCBC02t5LMNNQqYhK56CBwZSbkzRqKzAsfIJM6PgHBDeM73unKAVWVKZs2sHB++FmZ67VYcXrtqP1eB/xVFJpJNzWF3Z1J9Jt6Vgh/XOnHjq45mT27XNyVnH3GWr1+sFt1pXpWG9XFF3ixxdsNQ5r7OA66qPom2uYwYBbLtzjjtbc1XGNB5TuoODj4ykl9O1Dl2ssiuT84c7e5PskkvPc51uvkKIdLYmvXZEy3SxFWMJ/l71a/WYj6RpPSmm0Tp0M0GlcceaPtkZ11cpxuzyp/vlxgC/nR1J57QeLrbMZOLY2Im7rtqrWzEro/MDV7BiT2dyuPHS2ep72PFbfJztdzzNggI3QPn+gk5hrUwgR0CMMV69iDRNXOekO/JCz2s5bhcUf7vJOVPUqV72l3sG2TnTxOy+8f64/E6JFMnEsrQfuF1W+0nzVxvqb07rnFNM+leoakC+fPky/v33XzuW2Mf6QjyVre4X26Yy8vfLy8vPf2PSsphHDZ9uN9W/vHbvNmEf7N7jhGQsb1Wms/oegR3HaaecW8paKfvgF2Z6aozX27aT3Xh6ervLg/nq38tYltan/3Dmtttzq7hzQJ0Tm66zL+qj8vGxsJJf86RxRjvhgoPqY+px1ZOlHymnu4faLyp35df7UDr0+fn5p27X93DUS8dLb/748eOV3q36Ko3Kq3LUvXV+QnoEgv1QMtU1HVcld90r6m8X1GjfaEBU8lZ7OYbqL79L9jHGT9tWj/7ptSqvVpiTb6L9koITHTPspxV/ceXazPl1xxznKhPTfnRcsRnHzsyR9C/npurZ0lVfv379+am5Wr/VphTqfLJXnL+MU3S+c07Sf1ad4OwN6yy4OUGd152fxW/M350jUZMWFXbroQ2rsuve6BMH7H+ng8uOaBziFgR471d2PFX+0t/aDxy7rlzX/hoXJAC1X2kfyr6yz7VOjb9qLlQZ//zzzxu7oTa8zvOpIPa3yktfS8t2u7aT7dqF8+8Izgmdg6v3PMmoXIqb95w3Ooapb1b7oZvPHGurZd5lx9MsIN4t4xYkZyc5IildkkdJApd+5mw9Ask4XEFioBmA0PlOzwtrXgYAaeWCE4oypcmk8swebXDpZkHbLXCPn1SdTOfeRbIiWzI2dW3Wrke1/RFIQcZnL+dgD8755ULHTt5dUMfpeefwpCCAZa3WvSvzjp2i801CgWV05/W7k03tCW1OpdF3KLkyXL/zmG1j//Cbti/ZnwTaMKdr1XEc4+3j6WlMdVBZ+Whd13YnX2dfXBotw42HR9qblXH5EfHR5fuToMEtfdcOzmfjIoAG4NQbqzaHsrjXfzjSt9MVyea49r2nH+Pqop6459xwgfkYPv6hLtMy3G+XV+t0Y2E1Plht1ww67t1rBEoWEj+uLpWdbdP+VEKqFpBK1looSW3q4uz3GKfOf0iyzuDu0Wobuvnt0mrf3Su+uQeWiScSBQ5UqEnR6mSjE6cDdnUSOVl5TMWRbsgseK9rVFrJkb2Kru2d03ulfmdIVZGwXc7x1hd5V3oNskr5cDW4zivTrv1bH27pZLqZcncBw5UAIoFOAPuLipPGLhFlV+/nIT8OPiMc0eHgHJzk1HfzfNfGdE480+nHkQguXXKydMHDBfNE6ofu3KzdKW0XIOwQJGO8Jkf05dfu0Qh3L3UVWu1GsjNODue3aB84p7ry6sJB/Z7dL44H3hvKrEHBrH/Zrzzvfmsbnf+UfKSU/sDj9NHvBReRHdIihuqHIpd0N5TapOfn5zf2KZXrgkXVCRqDpd0vHemUiCc3v/V6Qmc/NFacxY6u7feKpQi1U3XMXaclM2VTva9lafzj7Ky2ycVbWm/VvRK/6HGK0YgUO/F69YuST9WOboOC883++eefn+16fn7+Ge9oH+suXbe7in14RXfeQsKkuaX3IdnIFb9iRSa9L2lOd2XttH/HN97tz7u940mdwRXSaOb4pknWoW6Ic0xXb27nRKd07totJJRTXGPkvwXvnMiroHJl+QwICuoYu3QpsEjtqWDByeRkmCnkKkfvD9twL7AdKq8+5qAGzxnf1EdpjLky3LhmILKjwA4OHgGnU4jkfJV+VKfe2Rl3rUuX5HQOes2ZRDjVtwsMiNn8TOSJ5unsbHfN6VXW7XQJHa7u2KWlvSPpVB+9xja5/mWbeG/YH86Rp23rbBbLmfkRepzuc9oBpXk4H8rGauCU7GGV49KpraHdceeZ5tiRX5jNl4P3g+t3Zx9oU/jRR+wqvT6axw/nWJprKmOd6wgnnp8R7a6OLrZJ/TRLmxYLtL2rtmYV6d66ctT/po1RmTRmoP3gv2g7cCylWJkL7TMfpbP1brxo/2hejqF6lN3ZS9bFOaI7nF5eXn6+HuXbt2+vSN8Us2s/aH0zXTnTqbO50KXjfFsdj9o+N9d3Zer8x1msd29bc6W8rR1PK4ooObg64a46w6tQBzORGinIpuPk0ukzwZ2SrjJqku3coJlTy98ryn9WD+FWdHV7ptueXMeajsqp7gu3YSrBVGWVAeBEd/dW+4ErENyirOdVhhQE3YJVA67H7JM6lxzWZKyZhkZ8Jc1xig/eE+r0dHPD2RV9lwZXEnmNQUByDlSmOk7GvM6vkATJcdC2KhIpd8WupPpUbzj9msqbyeECIy2jCwacDpw5lenRGbeyrXXTyXftdXbLjUntHy2XO1x1vI/x1tlOYyk9BqH2OS0e6fhn+51tSP4R86Q0x4b4BSC9dvD+SEG4I4h0jldaRzat5p3FOurzpsDREUulG1SnJOKASHHMavr6Tf3nxrrzObv6rvii2i9dftcO9UFmMU+l5ZMfmqbK1T6a2ZtKpySUlsMxlO6HyuliKdf3Ly8vr0injgPQ+pVsKvJJY+Y6x9ecpHjQtYtYGTOd3zArO/lctJUJ7j7txlfJh3Ky3cOerLTrql2/2zueZpOASpjnUiDh4Bq7ei7J7xzxmZOQ6nSO9L0GwyOQHMcxfhkQdYo5+KnIOiVCw6Rl857TyU6y8zfLcWMuHd/bUSb5po5+Ir0c4cf2st06Vnmuzs/adEUZHhzcE4kscKQQ9Y06/zyvjz+kwEDLpEyqF+r3zJF3AQG/U0ChSI45napV5+cKVvQBbeZqvq68Mfr3X6T7x4Wnzj/pgoK6Rj3Nut2HBKjWWXD2ze2SY9pZIJn6hGQTSSq2YebHJLuT7NDfCgYcPD74vXDzd4zxZg539kPnPdPpb/0TpAT61npevxPx5HznFQIpzd1V0sGdd9dSmyvtKkF0Ban8TocxjhljvCJY9H64nU+U28W41A2Mm7hgwXLTGK721Fig3Uj3Vu2MG4uuXtqRsjX67TYjOIKv6880PpL/0c2hhI6oUtvYxWn83Y3fHQIq5eF9vxUrMjkfYIa7PWqnFTvFvXLe/dZ0M+gE0sG7SgI5haQDTDvY7WIieUNCZhdpEDmFxeNb6lwxMsr8cyInookTmcqKq6+Vxq00uNUFrV+VnVOKrJ/Emco9e1H5rC95TutJQZX2XUrn7hHbkYiknTTHOT54T5RzpOQ0ofMzrTiro8+FDf2nmPRR0JnvAnAXNHQEVCpDz9Oudfo92dKUpkOSKf12eqRz9GhvVoIjOq16XcdOsmOd815lsn/U1tS4ooNd9SYSh7vV1JYRaby8vLzY3VJsT3L8k6Ps8mpa3iPneHdpDn6BvmTh2Nn3B+eCzm/95zr9rWmS3RljRHu0utNljF4PuA+vrzxqp21f2eWqedzvzm6nNlYZnY24J2Zl1nVH/iR/nWSJlq8L9+7eO11JO1LX3WL27BG/MfKOGdeeqkPHkUKvaRsqr5JybmFD66EudHEaN0CwXYqV8dLNh1Suytj5IC59KnM2N2dINjzJu2OPV9ImP3WGZeKpBoI63TogUocnp7c77hyiVE4hOemd0+zKdE5051StDJzVwZVu3mof7Dh6dHzSANWJr/dJJ46SQyWrMuwucFNjpwpO+4okl8qWyKT0qTRdcLCyJXmlX52h6Madky8RTlr+TEGmMUxZnfwHB++JVePl5jadOgYLT09Pb4KHZItmMjqiqa5pms7BS/OLfcD6Uvmuj1bgnKPZuWQfk05yhAvzuWt1XfUgV5pVH5at+Pfff8cYr3fT1jftUoE75Yju8W0te8X+OD2c7KRe0zwuiNC+7OpW+0nnP/lurMP5axynB7/Ae3Ps68dAt0BBEkptyJcvX36+OJwklZ5TMqqLccbwgRzHCcmkZH+4Y4Xlp9iBMqz4rKvo7IqLq3h+t44VWzMr15FPanv4SJhblNeX2FPHOrm1brVxLMPpd1cWbYUjjhSOgEz+C9M5v8ztAJy942kldnL+lpaT/JBd3dvdp9TnSQaVZebDzWRJ15Jt/ih2+SE7nhy4injVKb6C5CSntOl6cshX5Jyt4M8wy3trP6X28rrbKkkZuWOh+p5bKJ2BK0U+6/9OcVMZz9r9npOR/UiFk3ZYadtSADBTXivG+zjEBx8JnbFM81ydHecAVfCwoyc6+ZzjwHPJuXCOMc93zvoursxv2s/duq/Ux9907kse6kKSN5p+jLdElGLVeec17mTq0Pkd/Dj7oHJ0da6ugndyroxNl2+W5m/Gsa8fBy4m0aDY7VBi8EwiKu24ZX2rdscF0iSaHPHkFnBTEK64xRbeM1Z7L8x0ac1Xkk+aP5ESjlRjGvdb5UmkzhXo2OCGACdPV5fzexJp5EgnxnizNtGupDmwMn5W9a9rW8dfdPc1+YdXCLBurLrfKU5c4SwehWXiyW0RrONSumO8fYGrG7xcYeCgLLy8/LetXIMErlJXOjLSdaydV+epBOigdoGOO+ecLU4AlZvGx31cfXrNbe+7Gkh1BshN+DH8SnJd111mGiCQ9dcdR8rA631S2dTBV+WVjEKNTddG7iSq3/r8fY0rDVpUxrpW593uK1dft2105f6lNNo/JQ/vgTMyzkCmfnd9eSuuKF/+dnIfB//zIY1rt2pW6Z+ensY///wzvn79Ov75559XdscFBfrohNotDSzG+DX/aZOoF8Z46+g7B6kIZTo0zhYpOG+rHMqpjw+qbmQ9da5bzHBpOrvYOVm7oE0e49cupzHe2h7qf6fj2BednqAdpe7U6zttdc7rjGTS+tO1rh38zXlUetMFBKkPOhtCebTP3LHiip3ZSb/iF+3WuVruStnOrz14H9Bv5Efjk/o8Pz+/si3Pz88/d0DpZ4y39sWlKajtSPpsjNf+aL0EWsvgfO2Ip5JNg3eVi3ZAy1Z7TNvMPmUbHHmR7oW77jDTl6wv2Vy2m/512Zxv3769ar/2obNZqV8qL3e07cZzVQ6JnhonRTq5tqQ+02/CxVFdDKu2p475D+eJnGK8UucZ06s8GmPWteqHrp0unu/GYTeu9DfvscrKclL5bhxr/zJdahvnl6trte4d3HXHU4c0EF26FFiO8XbnEB3UOucGqHs/hpvYqVO7tFc6n3CKeYzxRuaUfkUpr9avbXWGzzn7rm9YJpVLBVGdgdX8M7lXoYZWz60YwXsEVmP4lXIqkK7NK0apU4DunCuPYyGNixVwPFzJ58phf9xyj26dy/fQBX8rZveQ86MIJA0Onp7ePlLnHpNQcmVHf84cg87Qz8bljqPZyZHKuKor9ZgO2JXyVuanzund+aw6lrulSIAk/TcjNW7RX7Pz7nEa5tmRb+U+0eGnXu3au3KPnA2hvCv3Yhe3zOl7yeDa3Ml1ddwf3BfuniVbowsamkYXS5wu7eDGAfXBTA/o+W48pXjJ1bE61/k7nXdkwkq5V3F1Xu3oOa1rVQfd4juk+rvzSjo5e+Ee8Xa/uzG5I6sjn5wNZxmaL8XKHF8kEl0e1yYXUzm5WO7qfezi7m7u0S+71zha8Vl38VDiacVJ7Rz+mWHmbxcg30oWOfJEJ2W3arwD7SOtj0FRp9CvBAOJVHDXibTTqMunSoK/NVgY4/Vzxuk+1D1mmU7RUE72K/tddyh1K9Jdn+xMcipYytsFUd25VdzL0XXB3XvUe/B3wM1bdfL5Do707o1U1k6g2jkDJKK6NDNbN3NeuzqIWfu4up10pvs9w6peWNVtauO1j2hLCt1q7Exm/b3azzs2KI0PHUfJGXZyrsrmnFUn06ojenDwp0FtjNqcOqfkk+6af35+tnmTzlzV8x2cr6zf98JMFqf7eFy/nR280j+PRNLRKb5019x5xmD1TVJox8ZXHV0sRJlndi4dz94XxfTpvO6Cck8q6TGvr8QYuhuNMafr285fq2PnB9Fmz8gth5Rn1tedXmG+3zWPxniHHU+JGHFOWNrZkzAjlVbIJ5XTOXZOKSbl59LNFOWM/EjXO+f/XgOq6z+VMT3m5qABAf/tQBUPlYMLLFaCGPZRMnCarnZi7QZZMzl0hxfPax84Io6P6a0o8pmhnKXt2jPDyv1ZvYcHfw86Mp96kI+D8fGFIp34+IMST1puqtc5Ec7B0DTuPMurcvRcNw+TfXF2a9Z/DtX+pBtdGbMyFc7BdjLOrut9Uhsys0V6n12/dY4mgwBe70DbUrK6+8xHINxxklH7ZEU+vW/uEQm9PnNYV+3M34ydsXLwcaB6UXUI7QyJJ+50Sh+F0y2JEFBywtmdWdDrfOGV8zOkOIDxSecXdnblyjy6qoMY+zA+G+M+78JJ903PMf2M7KG8jG1m9m6M/g9RnJ1x7+5N8hBusV//0KPOMzbUPC6mce8MVj9gBtf3hLOX/Lg8K/XO/I0VH4xy3JMjuIKHEE+OAFGF7Y6TQu6CATfI9MbrufpO20dZxoqjpefd4FxVfkznDF1y9q8EAAmu3Rz0rnzuUKo0bvcODabei7qugQHL1Ymj56hIOIb4b0Ru5dvld3lUzrS7KSl29kf6ZoAy227aKfgVx4KBb5oLs7akPkjOhktzcEA4h+/p6enVI3QMBopk0u8xxqvjKsfp08Is+HdOIp0G6rNqB+eda68jSJKj6h4nT+1SaB5nV9hPWtYtdmdGjFSapIccSZ8cPrUBySFMDr6Tu3Mo2f+0H7QtY+QAhve76+suCOoeR3C2x93r7l6s2JmDg88Ina9KMPE9T+4l46t+enqNiCLpfWdzXLoZkn5xZXdI8Yk7dnV29vgRtuYKkuyss+tPJ5fGR/yt51N+J6fzcZxMbuwUOvvEb8rp6nQydeSTy7cyH9SmqSz6SHnXLpeGMVLXTu0L5xvU94zAnPlKnZ+i+Wdj9j1wV+JJnVdHMI0xXv3ulHK6gXWD9GWjdU2/U+DMG5xYz9UAmY5+3WgGDiuP5SWH3gURDJxSWTOoE+7Ouwmeync7c7pHx5TNZhDGb335XMmRjNJKQKT9yICAdWtardcppqTk2L9cpV8JCpxSTsavU5rpfJozO+jmDdszk/Hg70QaC3RQODdpU3hujPHm/RtVrqvDyZUc8OR8dY5b2vni2s250tmTrv+ItMhR57gLyunWmV1bka2D0ylaHkl5pqHNdHZmRvp0u51S3uTXdDYq+T1aD32fWUDgZBpj/AyUX15+/YmLri5TruRsz8ioo9vPbqfPCs5Zkkp8v1Odo/2Z+euFWZxAG5R2mHR2iO1LutXVpzLOynW/U4yXfMTOH+7wSJ2j/qvGR6vzV/WlI3rq5d96riNWkoyu37gwk+Sr7xXbonJ3MqkvobEU7Y2z57poWHW59M7esJ4uhmUePU72TT+u7tV8rE9ldfn1uCOdZmPld+Bhj9o5R8v9O136PQMHwyxgpvNe6Jx/V/6KTKuyaz0MqHieQUAXNF1xYFzfpXOlYJxic4GRDn5nsIpUcmkcMaPtdsaLk3n2qX7X+qq/3aN+rm7XX6n/VFY9X45/2u3UyZfKnI1rBhRO1l2sKvg0J2f4aEr04P5YIWOo60p3pr/CTudd2Qqny2YOeue0sWyV3c3F5Ig4p3TVydixM07n0u7MQH2TsKKPXKDknM/UF0rYu/uZ8nV9nfQsbUhyvrmoQcec973acQW8j5xDs9VZV97MzhwcfHbUPC1yiY/YcfeT2hmSSd3c7Z7KqHMzYkLzunOqS1d03xV09sX5zun31XimcGt7UqzT/V4pc+WYj1HObL3T2bQvPFf1rOrrjlxxdvHl5eUnYUTiSAml9AigG6tceGe6GVLa1KcpZndzm36FG8suBlWZnFyzMUZfJvkmV+M6h6tlPfRRO/09C/ydE+TAjuXN6gZfUrYa5Lv63SBgOztHtdKRsHGDTo87p3+WfgduYiVCYGUCjPH2EQg9X4pHJ6xupXQK0fWTyq7XklFIfffly+tH8TRtyreKzlnX+pVY0va49rmdT+wLV3+nUNO13bYyKJyVe4KTA4LjY2XeOXvClWldoU7EUxqLusuWY5YGXgMDTUPb5GxQ0sWObNFrKwGD6g/97uyy5pvZrZW6FUnemd5IdVIvOjtToG1Keijdy478cX3n0qz4Q659K6vQM73KVeX0O/lMu3bm4OCzgnPS7Xh6fn5+Qzw9Pf1abFdoAD6G1yGd3tJyZoRTCj5TrLQTfzkk26HXkk3vyt3ZVTvGfd67RFDHlY51TzisIN2zjmxy2G1rjeEqz+0w2mmDI5sIt9FESSm++3YmP8csn4Zxcq60I/3u4nDOI7f7epbfzUUno9Mlaf4THynWeviOJ3ee6FafZ1glTGbXZuSTq4t5Nd0qQaNwKwElb1LYnaO6AxfwjNEbpRm51+XhJFhV3i7g0Tq5wjzrk65fU992E/hek7vIsCqTdazIcUW2W+V/jzoO/k7s6DdHOvHRB6ebODZnixmarr67dzCkslN5Y3gduuOIruo393GkvtPBK7gy71f6vpPFOal0vFew4+A5mbRO+hnOtrj+Xhl//H1Vz7rxshqgHv1+8CeCulHJJb5LkMRT0tc786QjnVbLmZH3Lr0rf0X3dUG6piskcmnXziTi+546iXraxS87CwGFGWlIUmomY/qonGqbrvSV+jxdPKI+GWO0boffFczGbapnpW917rO/mD+NhS4+TXk6ubXubo5d9d3ujZuIJyq+WhVeQXKmdSLXd7pRHETpXT2qJFyH1zW+DE3TV9l8zKnk1BVr93GPeFAevi/KGblu0F4h8HSSsP1URK6fKi3TaB/pPeBkYv9pX3A75hj/7YwiwdQpu8qvj7GVrPzWciqtew9V2gqtykZ3MTm5xhhv0jiFoePMlefGSiL39Jz7Tv0xK3MH9zQsHXYcq9m5362k/0bU+Kt3z9Q5ByWUauXZrbClR+ycLnDg++vGeD0fOsKJ192YcqvcyTGZ6fnSkWyvBkYsi33m6mfb3TfbTxvDPusIuMrv7pcrm7KVzNUmfZSZNol2zN1Pdfyrz8omaXtJMtFHqnutu+jURunj57Q7DEDTN98Vwv7R+qufKo3Wr/3oHvFR6FhQ+8Ky7hkEXsFq/U7mR9qHe/TL7+7bz4Ly675//z6en3Mo9PT09OqfUV1s4cZE5487HcMYyO0CcTq1C7Kp35yudj5kkptp1Y7oTuKCs0GM7bTslbnENrAf9L5079+b9RfR+R/1TT1fefQxMn7TPtRnJo/adNWxel+q/53uqrHP+1h1u5iM467alvqKu7g5tlgXYxy1OexT5p/dO1538Vra/cW5MxunTOO4BcLZdR3LOmbd2NVjlzbNf3dPeZ55dvrCYZl42i2cSrTOObgBlcosWWbPQavMKgcnvpZNRZZudAra9fosYHA3MTn1yTAoqNzZlgRNR4e86z8ipVFiKbVbz7N8lUUVO/uD98f1mbZPnXmXrupx6Zie/eiM2so96K7p9+o9XSl/Vd7Z/b8FKzpit5wr6OqelX2c/fujI1ydUUzjnnoz3UvOg85eFVS/XQHnE8mtzplKcOQVnUvtGyXu2D9uZxj188q8q77lyvAtc39HD6rNqG911PXeq1PM+7Aa5FEO2qBkF9M/+Oy+g2O1b5xtZN+4fJ0/pOmY113/SLrzUXJdtU3M95H66rNjVbc6P0+vMd1MZ1bd6fdMJu4u6XTSrXBtT4Sas7PJ9royVu2Iq7eLP/gqih096vTdTD6t18UmTgaSJ7ynrL/zYZx913jcpVH/oIvdS+4kV5LVxWy0i85GV1+mR/FW+o2yzuTnPXf9pd+Ux42vdL+cjXX8QxpHTvYdvab17egPjp1dXNrx5Jzl2aq0K6Mru3v0bRb88galOmayPiLIViTnjMed8ta0nBxX5FeFOcZ9+kADjq59Cuesq+JkGcSKknD1a7/NyqB82t6ZfAQd/y5NOta8SWGlczvXd9N9Nvyp7foMqH53u3+ukLluF49L29kjTcMy7vEuiVt0rNOHyV5017WflKDSby2DxzPHqgsGKl3K79rsyp+lcc52Z2d0/LngrnvfkpPD1aUr0iQ0SdSxvPrt7E2d42JLWtWlHXP2T4/1nu4Ec07mq2l+Fz6ybAfX0JHGBdWL7gkEzcOP008urtkdV04v7dqTlfQ7fnT3GSPb5BWZOzs+s9V6bVbfrTZ5jN4vT3kqDe+nO8f8nbxqd9QeOlInjVnKmuYL+5j1cRecLsLwD5Z0bNEe64JkR9aleZXSpb52/kMqh/VwzLn4LN3fe8diV8b17jydYZl42pmIXQdcMdgcCCWHe/ShrjunyDmjPHZ173R0Z4yYTtO7R0NceVTert4C+yW1j21MgcGtA47b7alU3ISn8lYHeWbgnEHsfjtD4ZRjt2rvytYx6FaW0yN0DBSorFIA4Y4VvAedw5UCuo7Y+qxwTsmf0rbPBEc6pQC/cy6SLnDoxvGKs5jSd2Ume3QFnR5y9iWd05fhOnnSNZ5Pjp6TkWlYZiLOed3tPkvOIld3k+6vx+i0nuTgEp0dUptHW+AW3Jzs7tF4109OLrdwUWl11xXrcn2Z+mBVJp7bact7gvbhd8tzcB8w4JzZkSt6esWfWCWeZnonpXE682pbXDzirrnH7Fx+zbsqQ0IXz8xwxQ53PqNbcC+4c7Qr+nE7e1ge+9494cF0zk9K7XHyuydlXDmcQ1++fHn1ah6+DqXKU5s4hn+lgbPJK/PJzf1ZHmf7VvN3ds4RVt1vJ9dKml24e3lrmds7nthRuttpJQ/Pa1Ce0nQO1Bj5HyC6m0znVetaqfsqOuW6EiSM4R+rc5N7FY4Q0naTea7fs+ena3zwmn6nFVhVlGS6O0VGx5B9o+9s4m9HCmm9JVeVpeU4Msqx9jyv8pJocu1kcMLzrq/ZBt4npnXBnzv/JzjhzhFL7T/4GOBYW3kmX8+l+eLq0O/kWM3k43kdc539mcG1i7+pO5xtUV2YynbnO5nqWB0xnVvqZDpbQfucAiWnn3hv3eNtdV7rVX2vDruST1WPBgKUx90LbS/bXP2uTrfagWQX0uMc6b64e6d2hHXTr6Ms2ucE50/Ss8kPm6XpcC9b1MnE6x8JH1WujwgN5hnYr4D6btb3bq52waeDs01dfjef9JqTeWZLOntTetPZlY540no7dGnU5sz8Y/ZNybd7Pzr5Xl7evt/O1evkqjH548ePV+8R7GRiLETCSWXR611s0MlZ1xz5pH2u7eU4SbG9yj3GsPZWy3bzIcHZ81m/6rfKqPVelSGdm/12cqY0u3ZhNjdX9F3C3f7Vzt38dFM0HctQRcgAW9NxAKdV8pVdP1qm5tt1eGY3xhkpKun6rWkc6aRpXJmrqL7TAav9+/Ly669fueqpSisNeJfGvcekvlXhqvJyacnel2wkZVy/uP6utiqhpGn5OKnmc7IW0mOC7l6owXBBR9VbbecqgXtcQ8tXOGea8jB/9/szgwHFn9Kuz45kQ2ZOyC118VPQ+dvZhZn+1bxXgtikx2hLiuTgS187PURdqbos2bLZueTYOSLF+QkqN/vL3YfSn2O8JZk0jf4u577yORvCOlw5rv20H/Vy8fr97du3n2lrXLvFPBc8cMHDBRC0F2U7dWdVsl3sc335eRc0dVC/qnOSZ2neE8c+/Nm4+sh0CmCTfUhBas3JFTlWg9GkS/mt6PxkF3fQpqgNcgvkjHVY9yo0Le1p0tVdHbP7tSPP6m9n17q6k1/CMnkvEhnSxU98z6DGJGxP50voblont44pbUPNBbU5z8/P499//7V1sc9cPcmXcHLvnGd9q2Nmlm51nq+A8/Vq/lvKUFwintLWwULXQWnSdCTRyg1yjhOdez3vzt0DSanO0s4+Y4yfj0E4ZaHlXZG5vmkQta8cAUWF4hw0/SihR2LRKSf+u44adQ1UuFuJwQxXc5UI4i4iklzqbGuZWp4ed48GklDTY96POk5BkCtL06yO+zQXunua0n5msJ0H7w9Hyrg03XlHHvGTtmt3TpLqjOS4E6sO8tVxl5xNRzRpQMCgoMgpTT/GHvFEuZKuUd2vtkDrpu1JAZsLMvSc7iZSJ5fljPH6H/dU76o+X71Pmp92uZzob9++2Z1FaRXa7TxO/e3GGWXQdroytN5a4Oh2qtFGUR7eU8qq8t7qnz3CHn0m+/DR5ftIqDFcn85mrKajL131rJS1Iu+taaj73HjpdsdWHrUrtEFPT0/WrtA2XYHL5/q82kEdr/qTunRWzxWZmIZ9qdcZjxFurDh97mIlPc90jOuTH5b0M+OQzperscFz/K3/aqz5KhabxTrJ5jnb5GTpxinnvbbT3XOeX/FvnQ86kyv9UdsV33Ll3BVbu0w80Smrcys7ijrBZs7jyrZulYvpdaClCfFIh4KKtkvXKflkBFz+nXaUAkpBlpZfaTrmezYI1ZlP5biVWL1fGiBqXtcHDBa7/nVEjjOyKrPbIeXys49m90nrYl/wuh6nPtU2zBz/JI/29yMc+4ODXThDX79VTzhnjY6Yy1NY1aezeZLs0BUkx0A/brFCPx0Z1TnHq32QzrGMbmdrfauNL6zco5eXlzePVOv91r5impKNdkTPOzuUgh+tI/0hSzmOGgzQ4daxw4UL1+8uTedc0+9wpJOry7WHsnT4aLblo8lzcD8km0EboTuR9HrZCUcc6fx1Ab4jnVZ3PO22z9kj+qG7SLGH88GdPUm2ZbVdLl+K4fS8849djHgVKb97IiHlVz89tUV/O9uq9l3HIW1Bul8cK65urc/FNGp7eM0ttrBvaHM0XS3YaOzV3TtHQnU2c6W8+u5Ip9nvK+STa4/C7XJ7hA27xTauPYe2AApA570TcEX4WYBBg+Hy7NSzK98Ye8FJcuidwnbbU5My35GD5czKo0Lr5F5p24rxYl1JRraHW4JdmtT+yt/1f8LM8HRpkmwr+VL+Wftn11LaWw30R0LX5wcfCysGeZafZaSPKzcFGLfamKtI+mvF+a/VaCWd9LfbNZXsRLquMrlyOvlW7QrrSH3ElfdZmSvtTHUlmdiejuxz112fOVm7vtA06tDXdS6U8Vx3/xw6kmrlvv1unXzsw58NNz5dUOp0vgtAK//Mrsxsznuj02k7diX5zas6dFfOND+TnC52uUWemZxOX7q0XUylSGMl1dPZlivtcedmY0frLNKpfIz67e6T65ddeWfjdDZGmT7Nf8XKnE66Y4WY6tpLf+5eY9uNras6bOtf7cjgp2u8Gboy8PT09GoVj9u8+ShU5R/jlyIju5uYbpUh/a78M0eSgyGl71hh56zpwHCDxK1Iszw3gXbgBpG2Rc/X/SvZ3CMSXX8o667lp3e2UA6OGx1jTMM+cUqXKwH1Xdd0x0Bql46/FAzz8cSSwfUldzIVSh4tT3dFaR+7xzB4j9LcWQ3eu3mwg/dwuJIyT/cs5T14P3BsdTpiDL86Xd+q13QXIfOn4KDkcfOkmwPU+c5RmTlvrky3C3hGGJFUou1xJEjXFvaDk9M5V6l/uYVer9f9Uv3MMjWP6k3Nq2k4nqrt3759e+XEzWw69avKX/ZDx5yu9qqs6QXk2gbV7bSHs1cgcCcu3w1Y56qM5+fnV+/aUBmrDTM7w/vI/pvpVqZZ0cW36Ot76fqVdh18LGgcM8Zrn5LX9PwYb20D9Q/LcL81vR53vmdhZTxxPqZ4QWMRB2dTXFl6nHbermBm81kXf1cf607S0it8LNL56TMZEjHi7B/9dY4NlZnn1LaoLnc2jB+3O2gWExVUXu7cok3v4hiW4f6cSd97yMX/MV7vlEo7eHlv2Mect86XcmW6Odr5Bvrt/CCCPoTLz/SuvZXP2d4VOVZwq53cesfTyqN1GsizwS8vLz8Hm3Moy6FJz5pWOW6yrcpJJ9Qp9pSPv5l3pSwqXWewOgWe8s0U/0wm7Q+2TRUy20gFtXuvVLGowqr8NOwkh9R4qCFUJZ+ULB8f1f7jVk4GAo48rfPaR44E4jHLoexM25WVoPfBGf3uHul1ZyB3nOikyHcxq7NT7vx9goDPhdm4mTlx6gTWbz72UOhIqppHu7bEyfvIMeic/c4BnekaV/7sWnL46Dzq42eqY/Xe0U45J50yuPtFP6FkVb2v53f6Q9uZ/pRC+4W/9ZGCMXwwwLFHe6d1FrrAQB/V1iCt2qHX0iN3bLu2a6W/3gud/dvNe7UclvXefXDwGrwXzg4Q1F/qH44xXhG06kO7x+1YRhoPyZ9cgZbpdN9OzJDyprhkZmOSrKv6gTaYfjh9XNWd3espVv3dK31SctHX7/QC0810q8YujFvqnLMzamdZt7Pdq31Cuer46enXYoguQrmYTsF40dmfsveur1SWKie1hWnoV2q9XRzl+oj9nMaA83XS2FG5Z+1z2NUvq+nv9q92CSuNpLJQOEcxERpajrKnqb6kpJJsj8Ssjk5ZXw0YCittnF2n45uUPidTmnR6rMqJjnWV79hvJ7OeT+U5+ZmOitPVxR1hbrWaBFN3L2Y7pFR29iWNeGewdgz9SprjUB88Gh3RpOfG+BW0p0BgJQjo5uuKXXkPdDufOhuxaj+4+tmBOjXZKkcwFbg6rR9eK7jgperk+yMqfdkyt2LL/kgrz6m/6ADWh7amnO+Ckle0G4580j5lXzjCqbMFHUHFerpgMdmej2Qfjr36++CCtuTTOaj9oC+m/w6m8/379+/jx4///knTBZKrZMdOmkeC+jztgnJ5ujKps5NO6fIUZnZZZU1lpDbMbCbLZUyUCIPCly+v/2luVo+W6+pRW6T2jzuKXHxDG+Tax3NuIV1lcfGb2zhQMjofjz6Ak5k2KI2R1O7u292HdE9neVL+NNa7Ot24X7Vxs3RXdc5diSc6VEoUOXQBd7oxGmwnR1XL7AaZ1sM0HXF1K6icZ9eTUnKTmGXsyKO/Oal10roPHd+kTFzwUcdk3VUxdjuXqMx5fYzxpl533W0B5a6nClQoj9tl5WTSttKZZ1+xLzlWeb4LKDql2ylBZ/hnisvlPc78waNBJ4Q2SOdGpVdbpQGEg1shdjJ0hv+94HTl6qfyJ3A38WwX9Bhvd8OqbnMEDH2JyqO/9Z5pHj2m402bRv1eeVWnJ5vj9KmzxbQL2jbdXaR1//vvv2/GM1dl3Y4wbZuWpyh5ktOaSCXXf7TBtDOadtfpfU904+TgzwTHvDsmnO9bBFLyyZ+efu220Dy60LFC3js964LK3wHqR/10dmalXKenxsj+ZeoP6jy9Tr+dZVFvU5aVdrBN7DNurHA22fnWTmaXh/Upvn37FmV3/UIZ2Cc8V7ZWfbKCxlQuXtNy+ecc7r7rbuUkI/vStY+21dkFFyczv+alv5Pq1N9pjq/EYCqnO38PuPs0w913PLFD6UBWR9IJ0VVoDj5Olko3xuttdtwmSIa1C7BVXt7kRyt0ldFdS8bM5duV023fr3JcMKDnOYF4TypPMqzctcMgwxGXHA8aJHB1mqSUysMJTkXMlXBVdnrNKR6OSTeG006r6l8lnBjwJgM4Gw9OETJdF8BovpX8ziAdHNwbqmMYrJcdqHnkDDkDgmTQaZsc6BjPnJtHIdmM2bmr9cyQtsgnG6Ll05+oT9qxWulWZHT6SZ1arUt/O1vM8y7woY1SR7p+13dBH2tLeVlP5yxTNn1vkz7ewMfqaIvcAhPnB/v3VkLnEXOoC6AO/lyoP1a/+a26ibGM+7id8c5nJunkYiaVS2OeggvSf8f4df2WbMtVO7Miw04gzv6kX0671Nnvzt8uaH7GCHrP0iI7y+x8fuej1LHbTEF7w5hGd9u6/mA7SeLVOZWDtkTL1fZrPrVF+oJyXbzRcmc7kZ2NZts0HdOwn1z5LGdGMM/mSYq73G/n66Y2O6za6jTmO9x9xxMVaaek3UBxDqo+J00Hz60EFmbKyKWbDcJb4SarO9/thpoZzC5/kqeOXeCmYJDg+r3OU4lysuqE0C2bel9ZNhUk+0IVV/XDGK9ZcB0vdK4V7lzl4zsu3Jiv9nBXlSpiLZNKvYIBrUP7Rw0Vv/Wag1NG3fmuDJXdKcZbg42Dg1WoXRlj/JxHOhfUfjAPnQnOC9obp2M/avBaOqELBJzDQx3YOTtd3WOM1h50wZeec7vZVogNto0ki6ZRO6t6ezUo0DbSLtOxZlvqm+8XZGDixurq2Kv8uvCjtrDkow1UG6POv7PhMzk+il2g/fsoch18HDgboT4gYxrqApdGySfnOxbUh9T51O0Q+QhwepJ+OeH81aTbZ74m/VO9F3q+4BaFed9m7XXH+tvZQep9HVOp/5ytoR2rby7eq0zOjrj4Scc1Y3e3OzbtIKtrtC1aPsdGInOLgKq5SHnp863cQ4fELfBcB5V9V4ad8f87sVP/Q4gnPaaTqGkLbpVTb1QN0OTscddMlZ9Wp52j4ZTUo5Cc/kK3PTUpIOZ115IsDMDqmFsZNY1eZz4qTqe06sN7pveFhGS1j3mpoF2Q4Bh3KmX3vg9nIArqeMxWKjSwoJPOe6V59blusvrJQCQi1423pAiTUtN7mtIf5/3gkXB6TXVIOfRlGzi3nH53DqbqKzp3ToZkS5LMjwTbQ6fL2VGec4sb6fcKnp5eb7en7ZmVq3q6+ll3s7kVzlkw4Oxn6dDkxCrJT7lnjmDVSadbz1UfPT8///x3H62PdteNRwZRVTcDLcriFi/0fKV3u6PcQlO6n4+0D8f+HKxA/bD67b4dNKB1/pizOc5GqQwuMKbf5eSgb/s7bY1DikecDV5ZLK20da7TuWq79TdlV92uMQtJ9RXM2ql6VeVwMUTyN+hvM87hjiO2m3Gz7mpyGzmSPXHx6Cw2KNCuMT5KsRvbyT8koR/A+aZ9ouWmc4Sb2w7dfOzmelcW612p37VrxUauptH6VnXPw18uPsbbTksOYlJeNUkSS+6IkzqXVkU7RcS874WOhEiTXEHSaWUQsFwXoJDMYEDGge0UdRewOaWoioiOMBVTUtQumNJ0Grxo3kRYMgClYmP5Koc66pXOOe+uverYuH4hueSUi/ZFp0zTtR2F5fKksg8OboHTPXrsSAnqBHWcZ+NfdV5yct3vHaN8D1TbO6ckLeaM8fpveQvJoV6d15pfCSi9tvJeRdXZlce9p2kmN51X7TNHkLlAQM/p2KJjyHMlI4/rXRs1FvnvciVv1a31s420JanuWdDMPna2LNndKw7zDLN5tDPXkq3Uawd/Jpz/xuMx8l/Hq43RHfVjvJ1DqourPN3tpGOtC1gd3jtOIdgGF7SrjdE4RUGd1aGbr0m/6rXuuOB2EGmbk1zpmLrG2ZRa8KB+VXmcT5/Sqk4nmZP0nLa55OjiK7afMqzC5WWduhmi0tY1t4OZvl3y9dwYZj3OPqbdZMnu8ZqLtWf2h7bTjd1Uxywfy1jRQSsyEw95uXgdz5xzXk+r03WsdbBcdR5ZlyOfnOzdtXshKVweu0nN64rdnU5aXhcc8RwVGq93MjjyKfW7c+CdAuDH5U8OeurrTh7usuI1984Nh2SIqk8oh9bllNosjavbpXdKbRaMp3MzOQ4O7oXO+Hfki3M0nE6a2YaZ3nxvpP7QFUJCnb50Lf2ezfOkE1nXSj2dw5gWN1hW6WtXbgU/9SGhlNqT9J/zabiDqvLp42tat9tFnORISHre2XfKx8dVmT456a5+Zxd+l53obN/B34FkA1LARnB3Lf99jIFw5VHiaow9v/2jYhZcKzobsks+za6nulZjpi4WTem7cy526OIsl9cRe127GA+5sfjy8vJqB9EsHr0nXF3Oxtandt5WG7hYxt2MjnRKc1/zUbbUxys2jfU5vqIbZztjnvWyfStjfhWrZRYuEU90gLgbJaXXlTfH6OpgItkwxuuXV3OLnnMgVE5ujdd6nbx1vSOtSEBwULlJz8HG685ZdbJqGk64HWXqnOIur8rInWvav87YuvZwQmu5BbfjSd/LxMnLrZtOKbsggo8VaLkcB13+ksO9O6qIKWXw3bsCul1PlV9R13Q1WmV0j0DwW2Xv4JTwjlF+DyQFfIKLPwt0pnh+jOwQ6HdyclPwfyVYSLbn3k5dslMuDfsurUYTqbzZuS5NcpJdvaXjVGe6RQUnB6/pfR3jl67/+vXrzy36JKJYVxcoJJRd4L/wlP5Xe6Rt/Pr16yt7mWSqNOnxCycPHzlRW6Jp9PEMt2BIe8o6OT/TMTHz2dLvK7jFVhw787nhAkN3nY+p1rhn3EL94sp2NsX5ZZ1u2dHDO7qqC5h363ckwcpc6dLsyOPSpHRuod0RE13ZqVznO5feZ3zbPS3CdjBOVt2tPn/l5Xtz6edoPM5ynZ5XOd2OXe0D5tO6Xdzn4qKujrLfDmlM87xDipuTj9fFwhxPvD8p7czGOJko1w5m48/9TlgmnhicOidnjPzSuBrEOql0a5yWzfNU5i4I5yAkOaLBPpW5pq/2KJRg4VZ+5u+cpR1nxBEcer6rY9Xpdr9duY6YcH2s13iPCnyEwe0gUkU0+xe5OldI5KT2iypLBpVUaKxH81Z73IvGtTwy72p4dcw6Uonbfd2KtAsK3D8RufHvDD/PJWWakMYoy3o0kqLcVbxXFbWr+6CHc7R5rGnp/Dg9yEcRGKTXOdX/1EUE5xZth3NmnX1ZHVszvd/l07TaDzvjuhu7KYBx7U/kQ/2m3UttVntbH7cN66pDuQABAABJREFUfwYtyy2gpVVT2hNXXuoDple51enW3U8l1/Pz88+0tHElrwYPWi9thsrhHHpnb+gDlpzc5cGxxuvaN2meMO1Kuu78ClZ8rRU4n+ngYyGNP0XyfVywqnaF9kbT6Q5BzlHqljSOrtiCVd9tjP4fXJMep+1bkbsjDJj3nnPI6T/ewzqv+lwJBOc76/nOLur4cUQO5dFrOn60fsaZZVsqDUka/nlFyaILGS5+cgv0XYzAmEbPOdI2+WtqU5ytms1nd78IzueU3tWn6Xlttglj5uu6a/RVXF76tWlOz+qivC7Nzvy8vOPpngbZKR8dmDpRXTAxCzBdnTo56xzLWlEut7R9FzsBSxcE8BonuX7zuH7TCXcT1ikQVXZ0ZNWhdvKoQ83Jrcdu9VmVplNUVK6aLxlUGoPU3/zNAMCNfaYjVu4/gw2V2RkKJwfTPcKBflS5B58fyTElVoKIKk+dgORcJGeO6dxixy06fhedbp6lfaQsOqdndov6WfVsF2A58rCc7XRfu3vDetWx0x1PSgSp41eEf5LbjQ1ni2nnaueVlqMy6S6oglshpz3o9L6zoWnlXe8dd2+ltu7YlfecTwcHDs4vrGPVuc5fo7/Mc0yr9TBw1HRufjNdh2SrUtmUt9qb2vDRfTpHDvL+cDGY913PJVJBvxUuhpl9XPlJHzP+LZkdcVXn1aYpSUWCw+2idXENYzrdxEHbUrKQ7FI7TMJpNsbYP/TTunm0ChdHsqyZH5Js46xOxSx2v3U+MtZ3da9im3jSCkgs1HWnXJNy0oHICVITnhObN0nzahoep28dLEnRa30rwc3KuQ4zB7XA3WIrZXX5OyWq52lsOS7qPvL+csWVL2WkTDoO9LcqxzrvtmNSublx5xTcGOPNTittN3c6KVOvK8A6XriyUHKkXUzO6a9y0nZavYfOOM7SOOWyamR3kebrwd+LFf1a80Hn7NPT08/gmzZJyybplFaiaIfGeGurKp1zyJPcq+h2XKqTuAslyu4F13e0qQ7Oqa57SP2ndfEanct///3X6jfW3clUx2pn1DHX39TZLhhI41rzl91w76p0bVDCiY90a50krZLMXKRwdkn7jqQb7zn7gzal0tXvlTFzcHAvMFBTe1BItsYFtDpX3Pzt6lmRz0Ft0xV7UOUnfZh0T0rn+nTFpt8TK/2Vjkk0qfzuz4C0vtWYra5xHHGnUWdX3DmNjVx/uDhZ02gsxfI0XikZnR2oNCWji5vod7n4SNtIrI5Jt4jD9lGGK3EO48jVeUv9cYvNYz/znPNnb6lnjNtiwWXiSZ1uDeqpcEoQDkT+BbAKT0Wsu5zKqSpHTyebY1c7BcwJo3nT7zpX+V2Z7mZqW3dvTKfAVoKy7th9uzJJChQ4wLnVv8CtulQ8nOyOCSfBQmeW16teveaUIh8l4HVtr3uMrsYiHQmSVO79Tm4LrfYxd3S5leeClp0ejdD7qSvSXTDolEvh1oCgm2+dXAd/NzhmOmesI67H8P8mlHS7G/9qd+ggUp/cw8jTtuqxfrRdrpz0+95Q59bprfrtPuX8qgNN+1O/n5+fbdnO9rp7kvyAslP6uLI64G7XEx8FUDmSo8/rajfUQVdCqsayBgoF2hu3UFFIixzsF9pVlaXO8wWvfATD+RjOwZ/5HAcH90R6VEjR+dNdkK+/0yfV5Xxg2rok4y6SfhrDx22uz2YxwUeYv11MpaD+YqzZlZvs3cwG0mdXwoTvO2ZMQP9HZdE60nibLeDr494s0/k/7lzlr/I1rndxirbJjTUX3xB8/6D2QcdfpPvpwP7Q/LOx7+Zz/aMty2A+d+zqdH4j/bJb0OnNGbZ2PKVKnELi5GWwXQO7m9AkADg5Kp2+P0AnL8tbaU8Kih2xwHK7G3HlJiWHkJOZaWfGMhmwrm49r8GcUwzllCvRoved/aGKiEqCxpgGv8bEt2/fXq1Ep2Ct5Na/h+7uGZ1+rYv94nZPpJVoLVvbqce8py7AIeGkxsTlm42R1O+Uv+u3GW7Je/B3QO0Fd2w4qF7o9Juu2GlZLjDXOpNDRaLa5df0MySnQM9Tt1GXugDnFidhF52jrd9K4DCI01VWp58Kz8/Pr+63LgzovWbedD+oP8uecaeDs0VaR+oDrYf3ifpbAw++AJbOeZ0nSVb9oHZY+zgtmFButZdKyvG44Fbr2X5iNj6P3Ti4N5yf6ILQDmmMc27rcQf17fQ3fWDKsLvrSf04tS2drWG7ZvbmPe0O0QXpXR763Nof9AH0WleHS+PulfaZ1s3dUJU2jbtO36oe1ZiLdoPnUh7tN9okN4Z1TDBG1LIY0ygRykV87VO9R+kffEsWwo179p/rY3d/kz5xPkSdT/xDknO1XY/ALfVsvVx8jLcvTtbJUMdKELHzx/C7SLQjE8lU+dINUqVZMrlrzrCQBKHMWs5MiTmHcscgdIHTigPHMmYDvnPIk4OoitEZHCV3/n/23na5jZxnu6VnMvv8T/d94sT7x11IlpcvgGxZTpwZoUqlFpsfIEjiAkB2a635eWoqCcq425Wtayre6RQT+U27MR4f9s1BKjoDlp3bKEqP5JVM6Cx4HCYFnICwrjulaKIcr9A9HACvw3T9oP8edYGctd4avlfJToAdAM5BO+jFW1EKOBhrTKfGb4cxqf+T8T/Vf8811ukaX1t/d4GnhFGup/JW+eoXx/bpKT/K7XqpcxyEsR5OxriN9LXeBm862yHh51qrtaP424GfiYwRxg/iqfP4EUDbgFwf3VrxtW2PCQNO182DHnSFkg3X5UnXXAueoz59yg2U5GuwPO3h7lQi6T3rY8Ia41nCmIQ598aXq9RhkDcy7JvYLve19VsXMOjGtcMh+izpvUqcXw4KdRhpHuz7dvYDZeBNfcsuYU7ZTpOPXWsh+XjGFeIEsS79WYWJwarEs/ttnne+yHSPfBvbOj88+a2+7uyHE57M21VKdd+K05dOPDnAVJMsneio784gtoORFkq6ToEv1kcDrztSN52IMp87Yd4y8LfQxEdn4POelZzvJfKJqiIqKANsct7s5PF3pzjTv8WttV4Z46lPVNhFPJrJRU5+fSLIvLndum+jOz2esVYOPiVnzHJwnipv2STllYJSdjRMdgicnu6dUnIozP/vNFge9PnJhoJ1Lw2Ntd7OWxtYO+fDGGdsY91r5Zdn3kruS+rHRKlvH41PHaYkXcdADk+U2uicjC3KuXSj37HXyapzCshfOQIVaHLwJTkBNqiTDBIv1oU+xWuisez+2dBlG27L2Jjk4v4wD4OAVa9PFFu/p/Tu+kEP+ija6f+1Xtu4vGZZ23PO/+3bt9ZOTo7pxJvtqHtgzVRHpz/NQ7K5f9f6tX5Kutd/yMCNdG+Gd7o76d1Jj1Y7a731OejXWi/v/Dr3Pel98tuNVfW3HvsiMY3+Nfm0H8420jxPNgHrT/5C8dmdHLQdwbHs/vyD/BiHJ+r8pFvWpA9EVH0na2hnD9o+ubouT2zIK32+dOIpGYCdgXDCXOeErvX2ZXk0VHbG/dWJs8s3Kd0TZfReOunHzqjdOQZF3r1xfipGO2FJ4bAM73H8XLefK2d9aZc5KbBOoXdBn/pO9VuWPjlVPHuXOxnrlm0y/JPTwvx2HtKutcvtHK90z3XdY153DsiDHkSic7/W20fhKk997+Z3Z3BNxj31gk+gpI2Trs33OAWTQUEdtHMOdobJeyjp0u466Wm/ULzT69R3de3NDb736RQ3ScakwsMKPPn3ZMx6N3fXvndj/XhdGf40vou80cGTrwljWC95MaYkfru6JoO+6/sOBx748KCPotIZnQNbeaa0zt6tTwXA04mntfoAwmR3Tb5YdwLrhHZ97fLs8v/q9WudaiypPJO+SXqsy+sAu8t3Or7u2f/o/AR+Xl5+bkCb76mNZO98+fLlFbbwtFNR5X9+fn7DS9oEr7Xl8iUDnxJOh0ESvx0umbzpvtbbx7/ZhmV7hRxMc72kNBd574Q8lqf0nj7emy7/q91a16N5dCI4SJ2StBKttKISXH2qDis5ptFAd9upzNS/TvF3i8f5vJg65c4Jloza3aROQaTpfspDPswzjX+PCct6x4jtFB/ecbVhSyXl+cQx7nagpwVH5Wf5W4FZSSWZ2ilKxnqBhoGnC5B5B5m8JBmm01Ucy5Kxx9f9v4eC6uro5v+OOr5S8OC07IM+D3W6NOmXbh5MBrB3rXnPOtfHvYkhiY/EY8frZKymsqeYa/l1PJ3UedJm0i0u2xnYf//99xvngDpsascbUxUM4j1vEuz65bEoHkuX8gQSdSxxwadO13odOO1ODznwZGO90+kc5+kRhnIS6BgYB5IjkIJM5KvaNhalE7mVl98deX0wzdf3os4W2/H52eiBcdeIOrKCRNNJJ+e3n8ITl9+/f/9x4ol5unnjtURK/orJ9t4pxlAOKZ/xk2mJf+PjpHNd3vqrK7/TSwlXXM66ibzb12CZkoMDLO/VBwknk0+Q+uX+dH1l/xhoIs6Qqq9fvnx5E/ixv2Mbyb5awq3kNxdedaeHLZuT/jJY5z54fDs7bqJubZ+su5Tf8+mEp13bt/TrBGuvzPvLgScbLFzgabKmRbzWz6BHCjBwcdlgq3o654T5bNzZceicm07ReeF0jlByaKb0zvHf5UsKiB86TcmgT8orXaf+Ff3999+vjmemslTS//zzzw8Qdv0cIxvPNu4rSp8i7eZ3rf6FeCybIvFUqryf5l0p5WS82ih3O+SRL8f1uDmKz3Lk8+Xl9XFU8u+Xmu+U3hXj+x4G74mSTo7MaZ1Xy7rtW+v5jA7KZyOvd+vA+rbh73xeO6muZDhSN7ou41lyOBLGTUZ6pVe+qy/DdN+6f87sTkZ5LaWTxmltT0ZvhynGJp8iSvWZeJ98+j18//zzz3p+fv6h75KD0MmU8rKe9sdjVmV8atfj2tkoaaONuJewPVFh0fPzc/zHokov/hz0Mm/Fs9dK50wUpZPDtuvcLinhT2qX5W/Rs6nte2BZ0cncftDvpbK5vn379sOW/f/+v//vR3qtGQamaHcVJWzyyapkj1A305ZNfyzgtlwX31/Uzbuk1xNuOE/9NlYkH2fnwyR+Km8XfOo2JLr6Jv9mxwMDH8lv4tju1nfS+UmulZe6mZsOzntqo6e+0kYhfloWfOSQ5dkfb4CQR//pE9+Ta5lUXSVTnsYyDtcJZ+Lc01MfsCLvnc4/kWdacwmnpnJOs3644ofx/s42cNuTDWQeUh9uwbXL/2pHISSjmsrGwkoLpbt2x6yUi+wMpHZcPinALi3xlfjYKdVk8J/sSt9CnTPQpe2uO6DzNQ3cIhueJUsqvJIdd4kSQLgPDkZNfUm7Ba7TyoTj3PFDJ6WTG/PVmHPnIK0hj1Vql3JIAdyOJrl2Mkjl72mY/wmUjISHM/GxZCPeO9G8Vy9AtrFMA8l12DBJwaQi41050P6zAev5DlfIm/HyiuHgdhNWdfmtg99D1HtJF1IPplNOt7RvneddUAaQdid4ks2S8k163vk8nrYp0uknbnjU2KQ5VjjiDRgSg27EIG4+mNdOvintZMzeM67/NYx50O8hri3aZYUp3759+2Gz+T2e9fH6TFjF9jp9ab5KdyU7jT4O6019O/F5djJKH59o2dVxqjOmunY6qLNxJ1knXhmESeO+1utTQldeer2jyQ+r9KprJ9cOm8qG4aknU8mB2MH5RmxyveZ7Gjf3ofPl0u+ujym92qrvtDZTnZNv1M1Vl9+VO5mTzDet+1voHmvzhI4DTzXhSEkBcYEyjYuUC9qKqzrmjnbGIg1nK2kONNtIi871kI/63QFAGgTLpqN7G1adwV9UCoR5HOHuvnk99Y2LoFu4a/18Xpj1MCpOxdqBdOe8nKR7sTJCnQI5T0/5XU5Vj+d8dxrPfbS8/FfVnP/pNEKnjDxmiaxEPOf9O9X7X3YO7qXwH/Q/4kYCjfdv376tf/7550ee2pXmo1p1jDoZJay/Tlym04mVZ618emOt1+us2vQ/kCW88ponsc/JeLOxYsPJ7aQ81LHpzxJOaadrU1rJyrJjoOhWYl3cBa2+Ug5MT/UkeVC3c/Mg9dUbSSe6mDu15K2zM7r+EDvYP9o+/GfgtX6eliWGce6y7+6D+7Kz4chztzbqXrc+HvSgexFPg5ctyuB06fHS73VNfZXsJOKWMSbZarbHmc76SNQVXnfUO6cbKeafMupOy7LOnRPerfsrZAxJ99N3XVtfn7TnoIp9EW882Z/piHq2xmuHo1WuqPOzOrl06YnnwpK1fp5WqsBrd3Kv8LFwhn3lP8pNGz+8ps/DwFeyK/j4O79N06nn0yCQx7qzJVJa19bUXuW3fK74eRN1vPr3DqOv0OVH7WrgObhUxnSU13r7Lps0adMR7rVeC5eTl8q644n1s+0kwHTyqK4rDx2CNAC7j4+l3puS4koKK+00d0dXT5QZ5fnly5cf/a12fFSW970b6wAO66mx986K63x5yY8+0KgmUJO/zuBOoJrS+JfTXd76Pc23XXm3y77x3wCTsWIZdA6ADasurdL/K05BkseD7kvUmZxbDBoxj+9b97leluuMBmMY6/SjreaxfhuvuJ4Tb9SR1q/UYQlX6Oh0usU6q7vXUWfo2HGyg8AAU+VLOFS8XCUan+wTMaPSjAVdHztnwIZwfQpzbjl5SmK7tnH4rim25X9nopFvrKn7a603+Jb0Oj9VV4cz5LvmYdrsY32Uww5ffrW+vTeu/Vcw8k+h0gf0Wb59+7a+ffv26rrsWgaS6IAba3jfOnmttxsb1Bn2Oeq39RXXWqe/iVHcXE/5kj3L38acZI8zKFC/u5dUp3vmp/PzrC9237t7E/kxZWI/+et8Evd76m9de2ytV1OehGVF3cEC8pj8FtLLy88/t3A9xD5+2+fjXHZ/3fcdFlUaZUKsLz4SzieeOrI9wG/LcFeH2z/Nd4LRidI6nn6bj4TT9/J/Lj9q1xnZNbg1ganM04LpgkRFnaJ2gIoKgfU4wEAj3YIsfrq/rva3FQoX2KRQu4l01QHYkScjg04O3KWJe8uEsvOQgn+VbucqzQ8auWutV4auQY79cfCIyrmIZdkG51z6twj2NaVVuRp//9tDyaYDRveF68yOBIE5rQ/LtjNcurFPa+S/bDintfmQy/0pYQsfc/BOMo3gtf433+vRo+QQUAdRF5Fs5DgAZX1lPeX1OOFc138S17939ZJxx34Z9zrcqe8TLEgGYPpM/1aX6rmVagxoPNNhNOZXX9NGUHJukrHLgBPlzXFieyd95NinOVb1c64bF6ser6O6rrLpn4gYxLVs6XB1OJMeM7HsLFMbsp2R+7vovXPzQZ+XeJKJTjXxZa2f64k4RFyYHFvmsQ1mu67ar29jF232TlczwO+1TX3XrS/XXf3uAlzGGKaRiFcs262vlE48Yb6ko3d1nVLqE3WX7QmPceeo278xv/YHiPmso+qZxjPJxnnZBjcyuIFgG4T12Bam7WS/m6dsE79dWmc/EHvYjn0vy62jtAau4lHC0eT/T2WcvyuXsHRXd0c7e+xeuHwceKKzn4xeOwrM5yABO8f7iZIh9/T09CYwQB67nYTiiYuKvHBw0+Szoc7rZNR7stxjYqRykwOQdpYdGa6yJ22dpFUbTkvj1O3irPV299jR7eq7r2mQp76T7xSMrHxd5N5zpr4Jzk9PPx/L6/72muupG0Mr+1pfJQ/2g3JKQS+OjZ2E5BxUv5KD0N37N9Ju3T6ck/uRsaTmvR93oMPNgLrfE5fqp7HYrR+vv7ROK2/9TsYFcbDTnzZIEyXDfsKXrh7n6QzRHSXjb631Zjw63O+chKtkfCij+fn5eb28vLx6/5F1vvGFzkAyolkPA13cDGGAyPW7r/5tWyptNvCF4MzD4Gv1ca315l+IiF/VTjnddZ/fxaffCVXyJs5MY2rMoFzJU4czfzpdMfwf9GuIvkqth8IXBp4qva590q97r4/HPAUZvOlJnUO94voSFiRbjOurC3T5uvt2e9YVXRrlwbbMZ6KUb/J3TunqeiTeG+u54ZF82WSLJF2X8LGuC1OTT5NsdtdLO6XypE397nU6xHQT50hdE49S3xmQ4hqotZQ2BdlPvuzcm07E4Y7fNBYJq9O66tZGqtv+YtrcSWWTjZf8T/eD9ex4S+U7G83j8F5MvvyoHakmLwfa12u9PdLt8g5SJKO90ovYeRuSSblZKaaBY/k0OSuPH+XaOQ+pPt+/hTqFlogKyBP3pP0uz6TsrfAof8twrdf/zkZ+HXQkCHRBTeYxUHZgxUWd2vauMvNwjqYIv3cqqk7e8+61A22+TsqSCnfnDDA9GR+J35T3v0CdQ1ZpD4fhvuR1RKypHWoGOdJusEHU9XSUNi+4toxPxiK3RxybjJ7JSEhYlgytztDvHIaEi9Zn1jE7w986tDOo70XW409PTz/e8+QTBlyz3ViQz11/3deEYZ0eTtjN9jm3HHDyvwIlmdQ38eTl5eUVj2u9PkngDYvu5JZxpoh46H4ngz3Nh4Qrxp5fRbv5+l/Bv38jUd+t9XPNUS8Sc+jrMNiU/I3UTqffa9115bmmvPmf9PrES/K3Ep/2g5IDPGGK63UbiU6wYaeLU5sd7fIk/VyUNqvZt3QKbmonYYTt/u73zjfrbCGPscvWvCys4WluUnq/5VqvXz1SvFa+hDcpwMrfxDD3n0Gx06DThMU7cj1pbXt+dPeK0pwpuRD7U992fb1yL9XnPr4Xjy8HnmyckoGkDNPudVeX22EH3cnEQ+VJSpAKIU0UC5ppNsynSWc+unvvJQfAknPQGf1XJ6mV+gklJV0fO0n+pmKdHJ/Ul50j4LHr6qISK6LCY78mMOBvKl+S+28jxEEn80FASjJJ9UzjSTklpTnlf9CD3kPWmcm4rfXBE0/JqEpr2CB+xUAnhqVTv9W++TZfrI/3nLbjycZIkh/r7GSc8vjeZOhY1gl/OjlPjshVomxoLFOmPrlwotv4Xdfuc7fJlvB3aoM2UfHFvlQaA69TfQyycr44IMd60mN47meSSccHfyfbyXjjsXjgy4M+gnzinMEl+i/82J4kTbqus3VZj8uTh7SBmk5spmvzY13lOpJ/1NnrCZ8TdbzdQpTTDl929ezIOpB+Sd3zEw0sWx+/CuYEb9K8SDp3Z8u7rPuQxrDy7v411XOHdhXbYPsO2npDyPXtqPOVmFbvBmUZzvVuze6wJ62/03Uw3eMa39mS/r5lfXXzhPW9p37TpUft2HCacGu9fQdFd7/SqfSL0m5rcgKSIDrHeprQSbA7YyjJZ6eAecyu46Hy+USQJ8NkcKa09yjo95Db47FIyyJFxNOc48KzA2Al1zkOic8TJeRTT26j8qSdYrdRjpkj/Z2BkUCJ8rCcK40npJzHzpdlkJyALn0q9166V3335Mvj/6D3UxofO850Arp/G/J6c32lbzpjqtalN1eYvsO0ZCwkXZ50Tmck8Z557xyCqQzb6Cjp0mSoONB0ijn3XJOlV+tjrKn2Kl/ioztlS92efvt6rbfvHPRYTzqddhQdzw5D2Y/v3//3L0R+P4tPd1RdtX6Yt353a6tk5blqcp0Ju5JMdob/iV1m2vF6lW7hIfHCujq+7o2pD3o7x6w70ynbWtN8rHutt/rP7fD7hCdvwCYHlDxP9TF/d7/yXLHj2C87yRO2rpXf+5TIuuF32Fudjlrr558qMa917kkf0zglzE3p1W5Xnr99P53w9mZ5Zyv5D5WMm9X/v/7668cj3UlW3WmfaQ45cJoCqd0pc7YzjU1aa6zndH2k37YLUtmE2+8ly/V0bl7RYTs6DjyV8q1j2Xy0gf/+QIVcinqt1xMg7VhTAdUE9Y7y09PrI+b8m8cSiAexBsx/t0gFa2OuylV7XEBpoZYciofuGGwa4HT8/+XldYScE6/4rHuWXUqnjIuuTJ73TrQOLJ6e/vd+CRq2nFd20jqF/Ndff716+bCdAgdCPQ+qj2zfgGFlyfGpMqWEi3ca8yzL/OwT5xz7aV49X7p3DLhttkMlTeWa5qkVtdMov52Rc5VO5p7lcrWeHa9JJsmR/R0G0b+NkqFWDrSNLwag1nodMODpkDR2Ly8vr95tU8Q2bDjxXzgLC6sdYx132Gqt1V92E1fIU/XJvFQddZ/80QlK+i7hVvGfAi9+ifSkC5JOLpl81HroDDTzWLyUM1TzhAYU/62KZW2U0chd6+17J2suWLfTVuqMNs+1SnOwZq23/x7EvvgEHsvYgSZOcl3Ve7E4Z7pAm09IlVy8s+9+1nh4fTltGm+OU7IJkqzvYSx3RJuB1Bn3HS+pP53j9aD70HTaieum9Dttrc6mTbrPjjXzJ51Q9rr/QdW2Wiq31nple17RxfZ/ujlIXOTLotM/nFf+9KdL9GuoUzp+jc/m+yOpW4v//PNPtAE5XukQRqqL1+4n/bluHpqPzu/sgg/p31mp95m/xuzr168xLzGM77r1oQpvEnWy6WygqtM4xb7QHuR31c013+WjnLwmuVaIa5b1NK+J1fy2H0zZ1XeHGwlPJxlTpglPLZdb7bybH7XrfhelE0/dIHYDQ4PO7XFAUnmnuX3yzXTyne47X2ovkQfPE2pKT21bVn8SWZm6z87He9PuaZXhtXds+UnRcM4pL1ouSC9OK9wEsu5LGTxFVvqp/7sx3+Ujn5MyO2ljB6CnxkBn3HxmutrHB51TWpPWnxVosaFbOpmGGR3ytXonoPKaap1SX3S4k/rSYUPSLR2edpRwMBkKbq/Te/8W4jxgAKoonQAtmjCl6k5zyNjS5enmTJJ/1edTSWutV/3ixovrMn4w+Fj3i/gIX62zHU3z5uREVKf/r+jYZGB/dvoTce/fRAycVBC65vzz83P8J2UGVj2Xbae6rY7sb1RdtkV5z/mmvCmdPE1rtOp2IMG8TM7te+f4n4JN9gt43en8FNA56avzTW105U/4Z8DGhwIqvb49VxLG+jcPRdjvSX2k35Z8sEk+TLePfcUHmtpJ+TpsO/XldmlTns4WTHSyzpI/esvavBx44g4Bv6vxWkT8N5UyABOTPn3iDw1H77YlobJNG5oUmgNMKfjRBYdSHpMDSV16+jBvfe8ckysT7HcSgdlR6Lr2cc1pfiRllIJTnUJLC2lagKkdBiHrSKnB3KcPqq8sU+1z57nIp5SSTLoTD5R717+kjE/H0kaLy06OBfn7zPO26E/h808l6jGfyvB3Wss1PtwU8Akq6waST0xV/qo3nQSh8cg1R8zZrZF0z7hj/hNmUHbWT6e4k8ajw5xbjI5fQWV//PPPP2uteQPHDlXNH58w2GFPCubv9GlnIBdxl7fmpDe8qpyN/6L6zb+O74z3tdYPp7vaZV7Pd/JpbDpxEFIfkoySDDsZmOwkufzvpAee/D7y+ufJnQoypZO2yUY3Dnl9dHOv8Kyu08mYpP+53u3bJDxIZJ/NGzQdH9PnFGt22OH7nxlrimoOpD+f4m+S7QLbMjUnJnun6rkqnw5rqL8dQPX7/wor2VefGvTpO29mMB/9KMqn8wGLF5azPeayqe+7NZIwhjy4L2kdpHaTXqEdm3hJ49/xzXoTDk5yOKn7Vrr0qB0FkhRdMnjJbKqDnfBx9SrnwaNzzTftcyLy+HcReSp+vGiYbqfBStNH+K4q6d0krXpS/bz/JyjlouLTCo0gmHaLkkFrwzYZyFR8VmjJWewMa/LJulneTivHyrvVHnfvLNioSMq2U6RWTA5IMd3HV813qjcp6X+zAT31zTriQe8j69ZkZPu75m49ylb1rJWd3Z0RULvgdPArjSdwK62ujS9pHVf7aePDvHUGa8KLksMOZ5jHm0HG82l8El+fhThmfBcScSTZEqmOpG93H+tbyqzD6jQXOWfsDPDap51se1U/y5lOu9EeU7dFeXjMpyBWXfv0yDRv7oUz3fq5N12t2334N2Pnn0C2oWv+Pz8/v3pPGvW131dj+zKtca9922/0Pbixz/wJZ9iHNI92c4u291pvfSZ/mF56xba5Mbo7LWW9M/3u0j4LJd9gGpcqYxu8+6y1Wpui2rrCK6/tFxSlQGjCFvvpHnu/VoTyeXr6+Yh++UIsaxyk7eINwPKfmNd4WVhEsuwmHc1+dHhmmyLVnzC/w2SnTWtgwk/yurNJTujWcpdeLl6UTj3R+KiJwecurSiT0+vHIzhpnF51Wek5uGW+yUOlpeAT+2xlaecgKeZOhpMidx6fWmE9O2PFEd7PQt2ipcJIgGylbsfAipO//a8TyUnwgu4AkIrZY1VjkninMnXb7ge/abCvteJpD0f4vYPG+Zra9Drj+qnfO0eBczKVTQaE6/7MBvjE52dbY38yeU0V0Xipez4N263DKk9KAW7rHRtYNlgqv/myA9PN/cTD5Gwn3LJBSPnRYSIOJ8xLBkndt4FinPyslPSkjdq1siyTrkxOAA3rknXJhnOp03dX+WdZOwmdE2JM+fbt26vAE+dKwln2L81n9rXWiE9M2YG+RYcmfEky/V14kpyLLs8DQz4PJf1sx5l6szDAj911to7JOr8oBXC6AFTxaZ+jczZPiD4dy9ofqTzcLPU67+xZ1pfkNf3+zJhTfSGuFhGXUznmt87vfBrq/uTX3sJ/51Nx3IxDa71+VNRrhxsi1U9uKhIz69oBIvsonnsJc90n5+n8vg5XdvKte906TXyl+k50CHl+73ro/LW6V99Jx1h2V+hy4CkZoTRoyxCnAZJeWGoFVWQDnka1hWIlliLubIP1dgY/2/NfMTtP5Us7BP5tZ4NpzmPF3E2AxP+fQF6EKXBkRZMMcAc8k7K0Qk3KnLx0i8nAsNbbv+50ZN7lOY4dKFe59LLaUrh+mXLqXxeMK3KAqhunyWGyDkhKupuXn8VZuEKdDIo+K99/Elmv04Dxul/rpz6t007+u+vJaE0v6HTwOwVyqz4Ge73rV7wRD4uKZ2JW1d8ZAv524CDhV9KTlGmSNdv0uHxWo78j9ocGLfVoMt6tN6dgf/3m6bxkWCbdUdc7uVaehGfFf7fzWlRryP8Eudb68Y4b5kvz3rvE5tuYZuocBNc34cyUd7re4dF7qHMod+mfGev+C0TdyPlIh7nWifXAWm83Ubu1X0Q7scqYGGiiHVvlk142FrlNl+ls210bzNv5L0xjAKL6lgL8JmPNZ8aepJM4Zgl77ZsW0VdOc8m+T6Xx/qk+6WRKrOk2qTvbgPOb64a2SvnVVc4+UK0j+/HE3aqHfNEP8p9wsIxlybo5HmlNGEsSTTiT7FLPc7bnvK6n+921n/IkXWA9Vek7G/EKXQ482ajmwBVjHCAaOsVwkSPoXLCOHNPgTtfmq+rwwnd+8pH6bKOnG6ykdC2XTnnbEUh5Oh54P/3+7DQpP8+Ntd72j8q5lKXTWZcdR94jP538qm3PLYNCas9gxPrq2u2Qf/e7k1XiP4GX+25F3Bksk/LZGTqdkfORjsF7aXIWPjPffyIlg7ZwpuZp4Ul9+EjE09PTm39F7ZwBj2O1U2XXehvAqbzGHgfDik/r7irvvpFf82jDx3WkT/GRNmOMO2kjpMp3fXD+z4Q3Sf94J7X47R43pA7snIDuQ7LhfcJ7J8tkuDKdbXgzrOYCg040MDkf0r+xdsaycZD3uZvd1dGtD/Znp3/drq8/kqZx7fRNx/MDQ34t2R+wTi1sIf7U7xor/otxjWX3xzL8tt1Fe6zyJXvRGw71TXvWfTwh9sH2aWqL19QvKciS8nsNOwBoPfjZ10bS+2utNwcvmD/pvrrX+S/8zWCo6z2hhDXJr0g2B/MSI+s3faD6N1nzVnl4at39SvhSbSefyDEE22SV3m0amjxOxrDks3X18DulJQxMtMO2aZ0xz46mtX/KS0fv/lc7M9YFpLj7SwVDhZMmZUqrtl5eXr8QkAPocnYS0qC7bw5IWejTREt8dDLr8qeyn10B78gLwYqV6WvlfxDqlDHLUElMdUx5Kl/6vTMk1nr9gn2O7ZcvX368wLXmIym9D2oCITsi3c5zivCzXzsjurs/KbTJUThp97PQ5Pw86L5kR8A7pnSOiSNrvZ2LXJ8JJLv1bT2cjBOCux0Y3++CzSQHst2PZPB1xkHCqi7PhFPm4U8hyoqGehm6a+3lXeVZj3Wn01Ie1jPp0GQTpHzU750u8kaaT3H8/fff6//+7//WWj9PPJU95X/QS/r65ATUhO+ey0kOnYxOMMRj9Bn09QNDPhelTWyfOOHJyJpT5UhXHXVasOY53zVo4pqt/GmtETO6edPpef/e6Z4i8jWd1HF68su4aTH5OKbE75+EO9T9HGPLgL5xlVsr2+gdtqQ6dj6paZo/9EHYlrGV/aw6ak47GOpNjrLlfNqLuJyemrL/kwJzhVMOaiXZsK4TP4dj0PnpKabQjWla3wnnJp7c9pTXtsaunrSO34NhNwWe2GjHMJmygUdF1Rn6nLRua1K4nQHZ5d8tvCttdUZ/lyc5AqeOw1pvXwh9oqDT5PvV1I2JjfvJkO2M/7Sw+b3jKc0156s0R8sNyFTGVsDpXSN+T4hPMe2oO/XU7bp37UztnRgxOwA8NYR+FX0kH5+lj38a2cAhOUBdDsNar9+7sdbbR7tJ3SnXtOGQjGFvaHTGxq39L+p00oRhNO46A2LnBLyH99/tLCR8KN1aTlH6xyqW7fqQjP+q3496XqXO6GTbdEY7fKh/86s6y8gvp4B4wFNQdc33c+760hnz5pt5r+DI1XvmN83HWx2z99J7nMJ75X3QT6IO8J9HMFDrfGv9XPs8Ycu5luzXdLrVNuVab09jdWTMSSeHjKOd/5J4Nl4kvDMfJ1hjPbdbr13ffzfOrNUHBJJf0vk86R7rnnwjzp9b9MtUxieIbJcwn31RrhWuqcIgbgQV1viUepIF5ZFk47wuU+0km9BtdbYf5TZh9clYeF247WTnJfkku3BaZx0PTOvW74lumug48LQb1DJunPb09PTjhAePvnGC/f3336/uJYdirfXqSJ53uFm+0nn8k3nr20fuWQ/TX15eXkXvrST9OEZSDPVJ+SuPlXc6zcO8LOPy7BPl7TH9nQZLKQAqpXTCzAGe6u/ulJLr4gkkGwDphNDEN9tIp/NoQFSZxDv7Vtf/3//3//3YNfMaqDq5fsgH29yNbYGAy1mOySi4xTA4nWu7uu9Vz5W6rtR9Kv8H9VRz0o8tWF/W4w8GwVof0zsTfBowGRQOXKXTMvUph/6ff/6J7zEosp7u+sc06uqEWx1Rf1Tf6VAxnfq4+l/OmA3k0h0MrJ8aOb+aqIv5mGaNEYNF1G+0E/g+qKR/11pv5Mf5tTPyO8cv1UteO4O3TjB5vn/79m09Pz+/OTFY9dV6KqrTHBxr8mp86nCNznkRT450hvRkKHf3aGuR385psgx/l97erRfj8QNf3kc1h3xS3Wum1oTX0ZcvX17pgaQTKg/XbrVJbKv5WuuE/gb1L21i94M+EANQvOYcSk8YWC5fvnx5o/u53okF7Etaf8zPPInvbm5XW5T376SkR6pvHl/a9Gu9fhTffkeyWWjj8+QuX8C91nozHp57Sc/yO2GY+5J4ZH08ZVT/WEdcoe9R/FcbPEFY/BT5vYP06Sqdto3bS9icTlC5r752UMey5Ka+x8J8uT7m87d1v+2JCRM8bs7f2RMpvfPZT9fjzS8XJ7HxGkAfE7Vhl+pPSscKLClI/p6MevbDZaodfjrjJBkwU5vmrfttZ2aqh22TJ8tqJ4/PQFQw6V+jzHtSnqm+XR4rlc7oN1G2xfd0+qxzThy06k5Hmb+qo3M62UZ3v/gmH+Zzp8Qsh9TGybzbtXWa50H/Pko6spzdtIYZpOE9byjYyOCjASyXdpCrfN3n3KTONSh3dbG+K3JJMuI98uEAXodFk5HRyaDj47NS0sd0aGikO3/3e4cdxoZTvVhlXF/CGPLbOQqsj8G0tNN8go9VdgqATmVPcDzJbPfb9fwJ8/JBv5YmX6QonXTi+qk6UtDq6enpxysV0pqy79H5G8aNxGvSDVO5UzJGJJ3P+1N545DvFU0njv9kSn5CUZoDiZIunZ5Y4Pw49ZtcN+tKGMG573lS68ZpxhvXWZ+ELSdY6776sIoPM7iuycd2no7ugTvdvOh8tatt7uySrv/p/hW69KidgxqMElJZdAEiB6jWev33u6yzOuWATOVZ621kNv2mMJMBnQI/vEe+k/Ll72TMp53xydDvfqf8Hg+XoTyTI/SZKCkd3rNSpWKyQrRicb1J2blukxWnx53lvOtL4nxnkDYFbxPfiT9H7Lv+UC5TepJ5118rrEmR30MR/0n0X+vvPWin+9Zar9ZPvVjcp0vKwOWctSFu48ePWlR5tp2woNPZxg7r46RnOh2dDKFOXt7dY/+vYFLqczc2yUChXjavv5smvcrxKp4TznTY4zxrrTc4cMJf1Z02ozo9X/lr15j3+a6amu/lDPJe7T4nTEntmwfL0i+K7TC4w5kkF19PMnzQgxLR/jLVCY3k+FLP1okg29c1hwuHePrIa6Fz9qxbqW+nU7SdP8C6r5D1vtfnDrPtV/G7w8IJB5P/NeHiZyLrL6ZRf6eT2pW38wUm/yXl68iYZtxxOwysrvXzlGBhDO2g9LJ14uQkt9R+4sfzgTjtk7jJ17Ic0jXb22FQl+d0jqYx4PetdaTfaX7W9aSrrtJx4MmR2s7h7BR55bFyZj02yn1c1J316SqWTwZ2+k0w4HX1k3zaICcvVKgGgEkZnxr+XhD1bfDq2k9HCT+bck7GrIGpUzyTMbtL5ziXYpx4o9Iq4vybjm1WWz4eW4+jVl0vLy+vFCZ52PWJeacgWJoL1X/K5IqCLeoMi075JcV3muezUae8PzPPn4U6nUsdyPejWf+nOb7WzxNP3rRw3qTfrbMTzhCTqhx1AXWC9bvXSMmhM3rMd4cZO3xJBljC+KrP+rir8x477feiE6woOXk+rfXWCO/KVxmfYu3mY+LTc2Anv9Q3nmBK+finFpXfAScGp6Z2fU0nPeXpvtmHtd7qSc+5bl0kfic8edB/m15eXl5tYhQ9Pf10mH0Cf62fLxTneuL91E6RdYTXi/VtV1/SUzscSDrmREas3xhpX8RYsPNt+G2+ElYnHPwMOLOjZK/73omtn+6t9foQhjeXU71un9cc66Q/3X56FNR1FrYQp7r3LHZ95zx2WbbD+t2XkhXnlv0j1mcbzdRhSZff6ZZtpXW2YMLVySfqxuSKHnBQ0Gv3lvV3+VG7uuZgFWPcNbPBXYNau9M+3ZFOQ3ERJAO+2qRw/S93KZhV3/7XBfYrGTn1bVn4nh0Tvx8iKd1UZ9WV/mWGfU48JPCho/FZqFMuda++S6EmRyDV43RHvdlGdwzTfPq6W3DuTwqacp4U0YBJCtXGysRDd99KP+0AkIcTBZUcBd5LCjUp9G7NnQDAZ6NblPGDXhu4Dpyk05t27rnTxg0O72x5nnP+e/5an/M66WL3I720Nq0tOxQnwG7ejGVl/HOtT87ASdDcazHVMZ2W+QyUMGGt9ab/O5xhXdSjnsOct1Um4dzEL+WbeKn2jPFVr+0A/41112e2dSpXz+FKI+5QRpOjXeS1lnDF+ZMM/xQMeQ991nX3mYj/pmWbOK2rFKinPZZOUHC9po0Pz8VOt6aNhHQYwDhgPNmtmUTJZ0knrswHNyHoC1GXGEMTT7afjaMlz88855P/kHwZyoBzzzqVMmVeyoPzasKY5COksm4nYWK1newI4tMORyfeUtvTaVrylewqr/3kAxV1a9Zj0cnvBHdSn7s8Xjup/l35k3tes/y+Zd3dFHiyUeGTSQ7cJCXIfN51cL61+n9OoYL278lJsEJ3Xy3cJAsrwW73wTwmJZ0AxkG5DgCKl1Qn5Zlk9TuICjcp2k4ZO19dOzDX7eRXmh2sqie1U+R57Ptr5X+qS2NXbUz/rDTJI+VlGYKTwSr12/2zAXWP+dI5Ae+9/pPoT+X7V5JxhlTzz5sZvF/GLfHEOFN5fb+umc8YZqP3xDHoMIr1nawtG+bpd7VbfZl48/2uT5SXdUrlt7H2GQNPyUi1TpwM2LRB4QAK51GSQdpZ7agLyHBMWL9fAs82OgwhzxP2JVnyutswSjjT8XOyFjrnlPfJF/Onew/6b5PtaKZXkJ6B9CLObZ9c8lyf1jz1arcGag6nDRDm6fwOfyc+dvJJfoP9jMQD5ch8CZPSmjSfxFX3/Uq/fgfZ1vZ4JKzxZnvK67rZXtKXqV2nT33w3E+8OW96dHvCleTvd+sqycI8Wo4JW12e84p0ihueo10du7nbyanDtit8TUS+vJH1Xuy8KfCUlJ+DRszbnWayYe5OdQEu88UJ0gWvWM47zlSI7huvU9usJynT9KE8073p/pSP+Z1m5+J3KeikRDvlWN8JMK1cpnvJuGV6aseUANBte953cmZbyfHpQMT8drvGXf/Xervrlk49rfX20ZrU584JmPJ1xn+nQE/yfFbazZkHvaVkKNOArTnJgK3Xnd9ZU/edzvI2dKxP6Rwwj7GnePamQYddJ/MhGSbmtcOY08+EXeS1ZMm+Fk3Y+1nmvXVh6WCPhTeROjzp9DLnp3dx/XuiSR+T/J4N93mSw4Q7O9663ekJZ5KjTn5Occb3Es4kzOf9fyP9TvvuTyLOT/5e6+eTE+nR0UTUKSl/5+gaOzw/JzxkvrpOPkK3rk4oYUC6n3yNCVcmfs0zf3fBmJLxZ533xgKOqa/dr8mmZ1kHONMcYZ0dn6TkLxEvzJPrSH3p2u/w1ViSAljp89dfPx+3s4/jfhGbug1N0yTfjjiv07x3vk5Wrss6YqpvqjPlSXPy1H7p6OaXi9f3zslMyur799d/ZVyP33GXmXkpVBvwNPLXeq2g/C9FnQObnAwOYNefNHmSUvVvt9PdTwq9m6zdDgLHxLL8jHSqvDrl5MVikEoKK913oJLkBW6ZngT4OoOY7fs9UHWfhnuST5eXbdrZKp55v353Si3NpSSb7l6SA8t0zsOfQBPAPmgmzxPrwnQatuj5+Xm9vLy80f1+qX+Vq7XT4Qx1qXVvCr4kpyD1h/mLDOz+J7pUNmHZ9CGGdBhDvWmerQson/o4mJ10wUdTwrhORxoXUkDFeOI6O+M3GW0nRAeWhnPqhwOaEx6k31d4SzvRrMM4ktqesPkKzrDeZO8kHv40HHnQx1LpvLXerrmXl5cf793s5l5hDQPVXk+cc6kN+wB1P512NC7RH0o6Pa2PW2RkfHG/dmVSH4whRZONWHIh1R8p/EnrO9m49W2fhmU6vOH43jLOJxhFvuwjOV/azCF1fuzUrucBZcS+dxs76YSyMTVhZIcnO57ZV4/3rox/T3I0r129U/3dWCU7puwRj+EV+VwKPHWUjPUynL3z2zmVFFoyupOS9vek8F2+y+P8XX+Ld6ftyiVFOzkKu/anfnUKvyvDPv0OBc5JffKC7xNF6XLTfdffGa7JEF7r7Wmu1PYJQCRwObk31TMdLd2VZd99vSs/KcKTOv4UQ6Kj9xh8/2Xq9ONabx8vrrR64SsDrYU5fp8HMYl1eb51v7sx9ekp40LN6ZOTp7u2Kk+Hl10+yjGd7EnlXS/74P6XbL07fS/a1ZXa2+kSG/VOm/KmAErHJzFut2to3ZtOZkxlnJZ4vJVOsdfXXb+v4kxnR0783AtLkl3woD+POju+Pv73VFOt/bV++iw+OWgs8qnYLpCbdPGEOdT/p8GhE0ondVmvcfrkqQ/ro4RVJ457pVGmyQZP9B653LreT+x1f1K7nX808ZnkNvE1te3H65iHc/Uq1nQYPPHl9OQfce25TEpPm2bu38TLVTr1pZIv+V6qPnVzxXlPfbcdHQeebOTbmKg8DDDR2K9vKjLmqzSehOJvCigpefPpo3ldX8hzUrCV7qPyNDjr8+XLl1iPjXSDm+XaRWstl6q37jsI9/fff786gp9ept4ZiB/p9FMedV3gvovwO3BG8Gf9Ntbdb7/ktHMUizo5p3IcK44nwXFyPCZlXbKqfMVTovTSTBopde3TU+Tt5EhlZ8C5D2k3nDK8osjvpfRP6jlZB1M9f3rw7FdSNxesZ9daP/59seYxnQPqwPqueV7liUPWxTVXKy8dEa5PvliWuoj1cTOm+lb3SxdUvbsgTekTyslY8PLysv7555/1/fv3HzxwDtJwfH5+3hqI5ovfxHOeWK7P9+/f1z///POjn0lXs84dneTrjKnSnWWHJCPZet/GaxHHjKfEaqx5xJ88Vd08tefgZzLAPX5JFixTdfuE27dv3358np+fX33XfKy2/KgCnWzKw7ZRpdV8qL+lL9mz/upb54SyTx02JzrB9KvO6akz3zkK96adE/agmbjOqBN9mtYnbGs9UGfQluX41/qhrqeufn5+/uE30D+yH+RTvXWfOsfryDqdtmenQ5g3YfAUGKBuq7aLfwdK+O+aHIvET7dmK7/HpfyeX0nJprWe43XNgQ7vq2/VD9oHXd66ti1jGde1yQGlE/3I9ULdXnO5sMUy4Xh73BOuca11vPA71cV6vnz58kpOtPWIm7SfbGtxfI0xE3bQh7OP2a0rz6+Okj+cyIeAUhu8z7nKj+2oUzr+e7P3Ok+7CXNLWqV3juxJe11+GlMnho7LpLInvNFg79L525PG5WqSczHR+D7ZPb03dQBzS3s7J226d7JAd3XvlOYJL+9pf7o/HYVd662zXPdSf3bO6YMedE+aMCDp1ORkuuwVXNnlO3F811qvDIwTnOnIBuHkAOwoySxhyXTNNBppxhim1+eKcfs7qdPxvOfrKU9n7Hd1dEbz5KBN48jxSkGpK0Q+ypGtzUAG5aqPO8xJ9U5yu0rvtV8/Oz3w+TpN9nZRrREHab1ZatucdXmdOd8Om3Zr1I5wyps2/EinduwpTfi76+uUp+sDx8D67b396GwM5kl6a+K9KOH4hI0dFrn9E5ue1J1iOqFuXPh9OucTT+5HOnVVaQw8MmjnwHLVRzxKG0AdD+9dH5P95qDThNMd5u/oJH8XZ7jVZiDd9I6nLlJW6TQua1FyZ48ne5jmY6eOcFZblV7XrJOUjsuxH1ZcaTeWbSR5dBMhKR7LjH3xPfYxLWzzTlnXvZqwXFCOhKeFReJY34N2C/b0EYSkTHfGaldurbwrUOnOz7wuRxDykc00F3f99HXVy3/x81/UWg4u6xNT6d5ab4+cXuX/QQ+6SlxvnHNep8YJ4wd/874xqXuke+JpAn/iSOL/Ks7c4hB0BkLCmZTf/ezWfMJ7l038plPMH0lpzIivpg4D63rSq8kB4Ph2huupcWt5dWOT5kByymjY2jnocNh9rbQqw1MiXIO2/zp5uh+Jkm24K3dPG+aBg38+JV/D+FDE07FMow3NeV/prCvZ+l5jXFv+bbuzm+MnWHWKKXTYpzXKfiVfrvNjkq2dbE3LzdhTdnB3gn/StxNxfBJvnUyu0KRPOps+3Tee+pRex+t00mmHNcmGSNhSea4GoOhL0Zc1P/7zisr75cuXHyfhbZfYb6LMvA7TmO/G7RQjjKEsf8UOu0rdeLo96y3berfQTf9q1zHaBZwqrTP+mTYZ5hNvSRAeEAuya4tlTxbJJBvf7wxCL6jdxwuMwYS65iNVdS8pGE/8j3QEpro9wdP9zjnjHOiM2p3hb4XSLa5pXiVe32OonowF+S0APq2LBgL5nQJUbPNBD7onJf3rdG8y2FEoPLHecxC1qMOBpAdsVE/O8gTOJ3l2emwi4gQfse4chIQvlrV1mscgbRokfUR5v9eAcZ/Tb/etI8o2Bd2TMeoP89DxLD6m8Zvke7XfDjKttd6cdK7fPKnmx+2SsT3xmDCwZFE8+PULqe/VB/Pg37atLI9uTpyQ67fz8cC/P5s8T6jz1nqNNSkI+/T09OOPkWhDs3xn+3eBKOIOf1eenU/T9dO6eiL2ZbfGU9/MP9vsZMD7CXcnHUC9wddL8N6tJ3qS7XtCp7oh2THmc4c7TnP7zFP1pXvOV9cntkqldyfPfBq6mx9st+tbwpWyP7zpwcdWjdEJhxKlNZ3G4L3YsFtXaf0njJqoszMnnJzs11vpcuCpE46ZpxOQnAYHpDig0+moqis5H2lwJkObSq+bPCdOQ7qfAKcz9K2ok4xY3vcpvxT8s3wcxU8nw64q6KtkIJryFD+dMjXA7Ax8luvSrWC6QEzHb1HasbqFyEMCiZ087BCVsrVT5XxeZ24nrf0HPehWSgaI06k7HOzo9GLd2xnn3SYA6zdmJCxy2ROcSYaFZeG1eGIYG2+IQwk77BB0Do/bd71r/XzPHXlMgb6dUTvRzmiykXtCpf+SkVn3dr+N2ax7wp4pz04GtneSI+BHhugMUGaJP8qm69ta+eTGWj//XbI7FdW1x36e4s2JjcF8bndar+brQX8eWU88Pf18L23aJK98nNe2p9f6uT5K99lnYVuey53vMOmwpNdTP63XOx20Vj4Fc6s+ov6hXvSGCNd28u3qOukD8sj1meRxhTp8tb69pc7dqRrmdx85v5hu+XV6eOc3TH32nCW2eLy92eH3PU1Yw/Gr72RPVL76sK/EJ75TrPguPOI7n7o5czpOJ/amy/J3ksuJfZjqYXnS7vdEHV5eoUuBJwty53g6aJTKJIXOMp0C6gbfAzadanL+xM+kuE4AwpOA972bMu0+u8/8d6YuKDIF56qvLlf1fRQlOXQLjfmKThXlDjCpsLodKs5R53V7vP4oI9V99rVB2GW6wJWDw+xnAi4fZX4PsD/oQaYdcFt/WW9WnipPLCkDKBlpbj/xYx7I54nBT97qeoczbptretIrO11Lnk83MaxfKAOXq3r50tGnp58v6LyHTuz6m/rN4ArzmSZZT/rUR/2pW1OdxcOEWZ5zyRFIv6u/3Q50jUs5BV0QyvUSB9Zab7DBmxZrvTb4U2DXTkblNfZMdlga+5Rvqqf61+H3Ti896M+iNFfT5jYDrHw0u9J9WsJ64GTd2i7ryOvIvpTXQtd2wqJky9Z1hzcTpky/6dd02GcdxD6SOp/AtnHq245O8nZ6mH3c6QvL2PymTzoxmsZod8LJ/XT5ae6yn8bZhD/GIstrJxfjD0+4cUOdfHtzg/JgANRrKvXTY5PSjO3dfE31d+kn8yelTTbCrW2lMlfp8jueOgM6MWHDvjNybfBOdU4KipOo2q/7Va/Bg/cp9FS2k4kVjQHBipc8Wz5cvJYL0w1uDJ50CiydXjHP6ZjqRzgIVlL+PZEN/tRX8m0HoAPaJBsbnMkYdh7yY8PmVD4d2UinPHZ9d9667vj0aSnz8DDAH3RvSnrThk8XUF/r9Ttmiow1p3x0aWnNWxd3myY7nGH9dX/6rPX2JZyJf+rXtPvcbXpwgyPxxT4kGfv9Ck9PP/9Jjxsnt1LC1uLFuFJzYtLFSd8TNxJOTCejqK+N0azD6afY2xmW/MfH+uY/2nUBJzoDnW43trBd2hT8V7sqY9srOf9uq8Nl9zk5MFcNb/YvrfO0bh/0Z5KDptQVyUdINmGnSxxcXWsOIpMmu9Q8dXXz/pQntZl8gInSumOAueMn4aHlO7VB3stOnXRod7+jE0ytvprct66thAvkkW12+J/uJz53+JLKTxiQ9O600cHPpLPdhn1TP05p36YCUC8vL6/+/dEbfPbnaMdUGf8DLuXisTE/E6X5sPP7prFI9sSEfbfyeC+6FHhaqw+UmGiA2DBPCjwZr8nBYNs08F0v83eTZmegpAVMniZA6Bb/ZOSnD/m1s1AyWmu9cSLIb913/2vh1eL7/v3nX0paCd1rEtKwdRTcsprAkb8Tr5NSTovU+TiGKc3kPAk83iPDE6Cpax7DtUK0odXJwJH/q8GzBz3oVrLenvTpWm8fYaj7HeakOjsDkfrUmHaKM5Mhl+6fGMSnDsHppwtEJYxJ/U56lYYf/x68MLv+eeakz6dEnK2+8S+RJ3vFfeK9TtfuPiW39FiE5dbp96mv07xLTgCDTik4Rxkal81jYQLfc8M5xLIM+nkjZIdFaTxODOeTPCdkO/Ve9T7oc5B1YcIe68K13m6g1xpPj9CZaq3UdX2f2LCdDiO/de17aVNkomQzpzY7GVqXOA/LT5T6UvwZP7qgBANPhT9X2mSa7Y+dTKqtU93R+RvGl24T3mVSHV29qczE9+5UE7En+bcn/eeYcc0Vb8z39evXH+mWP4k+krHur7/+Ws/Pz6945NifzNnJ7ttRasP2VX0nP++kfuZP67tb8/fAv8uBJzZ81VhMQZATRUjjn+3vfhtAOsei0tKObVfOdOp07CZGUuKu3/X6VBSN75JDt5vIa5/m6fLeg5LBW+nvIfdpAk06Bt1pMOd/eckv0d2Ve0/gJj3ethuPTklZkXWPaaa60u7Agx70K2hy/Kz7vCHxnjYn4yHhYIcVpzjT0YkDkHhzWocv5iGVN34nB83tpUDFy8vLK6Pvvfok9cO7lHR6TmhnkCXDr0vrcD/paJfveJpsCTt4p5+S3VX5lDPw9PT0ZlwdzPIjDzb2act0cj+xn5z2XnqvLnnQ56W0Fnhylo4urznX+J4Y41HZjEVJpzgI5bxJH3S+SdLpp2XZVsfLVUrOO3lI/l2Hu901f5f+o1/pdioQdYV38tzdd75qNwU+7E90mHE1qND5nNO4Gr/MY6o3fbPvxiHL5cTmsb+S8DaN0fPz848x5kEMb6751KPnhO2GE76vknXJLi+v09hd4Y3zK83rXSzgVjkcB55oIHgx06hwJ7x4/GhYCijVrmjada06OXH4jGblLxAo3pPzXL/5TGj6l5XiLS22DlwoH6f5N/tNmXWnq/jyw/rUTmYZgXxxXxl5lB3boYxpDNKg5GMR02RMCohpu11W18kx9OkolzlZgNWHNA+6oAqdWPbHRjKPhFLhuy4rZcuqAwHKNRHHivzZyDcIVhs+RZeUs9upPtYueseXFRl//4lGfTe3HvQ+4pqiwbAzgqf1YueBujUF2r0Wq97Sh9Sl1os7A7+b58Y769nCOd/zTm7nwFSaDbbSq37UxAYwy/nUSjcO1gs+Ku8NhyQ/k+0Kypi7qnyvFPMknpMxnPLb0PPJ5SRr6mKf7Jk2ItwurxNP5oNjlU46eSfaeETeyTfxl7v4XAc0/CvPly9fYp//+uuvH6eseXLE/av7E04kmzPJsZsD6V6SvfGs48l2z73ogTP3IZ944Ic2tvHHujn5KGv9XOPU1bbtkk6ub2KHbUN+bE8XTrCe4oMnTCsv/Qjax1y/ide1fuJS5TFZn3Rzd9r8tB53mZKt7d0kO/bd+N7ZEAlHSJNeKZn7vUOdDIj/qX/0MVLALpWb/CT3oeu79Vzn233//n09Pz//+POK+vb6SjiU8hYRIyjXwvriicFfEgNRnLN82if5anzpeHrszphpzJ9k39kOnd1RPLk+jtNkG5CnLj91gO+TvyKOxxVcuunEExk4bexK3rXyS8Er3UGVqn+t7NgmPjqB7hTfRKf5urLJ4E3g04ET+2DAIW8FqgkEi2gAElgIfqYOPFNat+BcLsnp9PTQyXybDMfTvDvguVL/6RrpgCIZB11+A5zzGLSYvxv/K30tes+6+RX0J/H6byUbjtbV1B3JKZjqXKvHm0TdDqbzpI2IiSZevW7Xeu0ATYaz2y9coHGRsOIEsx1UMhYxwMfAQhmO3EQ43YVOffG3g1q7ehJZ5p1+T9g5kefxR1DnrHV2hu+5HI1Yn97whtnLy8urf6+ruugMGmOILQzosR1fJ6Oedl+nI27Bqa7eq3btgz4fJT9iIto/J7jhvGnOTLjm9bjDt7SOr9iVxpErui3RR+g42/8O7Ff/uxMu1FtrvQ0KrtUHYXbOeMdvN/ZuL+m9ZHtP49TV+dG6Kvl0uw+Jp6LdBwZEaq04GLfWT3/V/5pYeWm7+Y8BSLfIasKmjuzjT3Vf5anD81Pa6cVbbZjL/2qX0hODFCSDRbxHo9QRXLfVnUYxb53iToZYN+CJ904mnRKajP9p8SWHYMc/xyDt6ie5cTfTabWIJwVd7ZHSHEgyoiJgft5z+r0oOWq+7/ngudWN7+8yRh1AKnLUmsCVdkaSEu7qrnt0JCyfjxzHB/07adLvu3VlPdoZ6AkjJmzr6kjtdfjT6cadzuh07C6NRFkkXdvp3KS76US5DZJ3zvzvMww8PD8/v3q3YNen4sVBpcKS0l/e1Ut17GgyqDpMrf76cbPqZ3IaTvi4lXZjSdml67IDKO8kF558TZsSdY9YQQfAf5FdZTrH6lfjSbK9HvTvo05Pp9OKzk86xagJXxzQ6jYydvrhFDu7PnT28nSyNlE6JcnvK9RtOnMTxBjBE2yVxr5ZB6X+JV5PN6w6edXYdPZ3KpPK8zrZ4Kxj5799BHlzaK3eFup4Id+pTBHnGD882VTjnU6Epc2QjqdT2q2/5HMmH7Sre/p9Tz4TdfbQRDf9q113rzPMuRC6yG+qeydATqhknHT1kY90vzvR0fGUwCSBWMc/wc0GXMrjBes2U5oN32ksDTZpnFJZBzqSQ8P+dXk7md5C7MvpSSmWTW1PTslpPgZrTnnxtY0h7+g4f5oH5se7zl05gwD7c6VfD3rQCU3zqdMza+VAvnc/WU9hA+ezdfGEWwkHu74kvou6wEatRec5AX7jpQ0zy4abReYj1dtR5S/DmjhXj4VU8KnrB9uggV5GZF07cOI6zFc3bzo9loz2FCRx3hMjPzkJ96Y05nYI1nrr3E3zkXxbpnVdp9lqzKsNBii5VicbzPSRePMRNsmDPjdRh/O7s9GLrjrytrNS25W2s60SNl2lDk8mP8HYR14SvqXN8PespaQzO92VbFd/025Y6+37iBOd+BQ+JWr+070JV/xx4IrzJdFubO9BCX8Lrzu/r/PTOr9j0sllw/C76q2NqjTnE9bRXryn7u9syZM2Jj2wS5/6ccUOuWJ/kt71qF1RUoie+FTaVuD89gmjLqJspcz0BAodv2nycvATEJD/nVwoC6ZPxv+Jg+C+WkF6gXZH2JPSdHCLdSWF3PV7105SPlNfkyxPaGcQdA7GBPRrvf0r1FsU0hXnpJSyDf3OISgeafxTnjT22Q8Hhu1ccFffa77qftCD7kmTgWbd0QUPrAc7538KChSlYFRn+CQ97YDYtHM6Gc47wO/wLGHCxO9EydipsmUM85/PqDvqtx//tmPCz/Pz85u2jRfpNO1JP0w0hDt9x9+TIZvq8Vy9N3XY6gDdTk47nGFawvV6X8ZaP+eG//GwKGFSfZJRfGpQv4dsv12lk7F94OavpQ5D6rvDC6eb0lp2ed+bfIqEb7u5crKmO0q4Ypuyu89+Ugcn7Klylff0MWv2kd+JjC2pP8Ya+6OpzUS7NZ7uJ/1vHdfJusMZz+tOb94TbyYs6fy5yedK/TYlrKFNVrZMfRfm1LXxyYEozseq2zh0K6VxP7EvEhmbOt6u+munNkk39ya6/KhdZ5ylfGbCpzFs3HZld78rLfHMdlKZzsi2EbtzRpyfxtSkIMkbv9d6HTSq3zwSmo6W1oIqhVvl2Bbz7RY22+LkOj051CnuabG8BzRJk5K1EjldYMXflGcC7BOZJ4Vsnp1eVO/WmJR3d3opHStNj+clRdPJ8WFMP+hWOnUokwHiepJ+n9YhsYHrfuf4TnloFLlO3uv6OBmczOcy5LF2/ajHua7t7J+s345vt+O611rxsbS6To+c82XlNa4JUxI+XTG0k0yTTKZTpjZWy2HzYx3JvrknUU60d2gPdGS9X2n87spxDPn+jZeXn8HD6judeG88JszrMPIjZLf7fWVePejzk30d6q106ml6iqO+08c630Fp4gjTUhtM84bCexxI/3ZwbFevdU6n129dQ52P5oBCXSc9luyMEzxmW53960fsun4mmRo/Xl7277dNPk3ns5/i+y3EseXviSgDbtDV78JOlyFGVV6fpvU3/5Asrd3dBv89ZDdhvuvv/KrOn+5sEf7eYdtH4trdHrXj/aRwXY7BmZoAuxe2eiLzO52i8q5E5xCkxbHbGU/lLYOdc2JDmRM8PfLgAFXxZwO/rhl8KqVFo9MGnxUa+2/gsUzotEyymnYQ0tieKKx7UlLcidekeJi2U+yWaScXK5DOOeuOFxOsaCyl0xZus9vRm/j5lWP1oP8mJcC0Mb/W/LgO5/y020qj2Wsk6fDu0QnzmfibDESn8V7qY8Jbn24qvZBOw1JXp0fSOwcrkfGFj8aZV+KX7QPyTlleNWrN164cDVs7Z8lRS3rTRm2HlZPdcIWSXCi/HSXbZ5qH5j+Ve35+Xi8vL68e4eTJp3TSKbVr+lW4szPUJzox4h/Y+fuJusz2Ev0K24hpczjpKZdN7VlHF6UTqd7gTjwmsq2a8M+6zbb/tD7dH58QTj7XCZ1sZLv9naOdnPwOi11/93ut+amQRCkwtrP/jTs+tGC+p7G7J9ZcIfeFsuSc22F4napmevFV/05Xa7Ie/bbMzZOxOvmH79HbHaam+dRRat84zO9f7VMnuvyoXScQK1IrGQ+SlQLvW9FPbab6dwZCUtwGgKlcKfZdoKyro8gBAPfHSttAODkGVYf/WYYT0pOQ9adHH8zzLYq2WyTm/bTsjrrFZ2WcHIUdaCfF1OUn/6cKheW7nZsEIN3cYJmkcJnO9ZeUL/npDJDfrdwe9OdSmksd4HfGZxED9rc+Zjfh1FSH1ymdhyuGC9dtt94mR8BysNOTHB7LNTlMbj+NRaU/Pz+/+acZys0GIXc3K0Bmnpk29T3dd/upH10dyRBOTsKUb8frJOurtHMOJqfG+Yo49z2XfUqkjP/KU/OL85lzLuFQh6OTbvgoemDbv5dsf1uvdXY452C30cc2drjlvJO+mO5fcTStt7o6T3TYhCtJrlcoya/ztehDdfeTw3+iA7u09Nhkklmn4zvb3ve7su5nwqaU515Ys6M0x1J/PRaWa2HI8/Nz69d5Pa/1049N45Q2P5x+K6W+2b46KWs6sVdSO/fCsav13PSOpxMH2pN9pzhP86yVX/xW6Qwk2dlIwaKdUj5V2l1AJRm6CUispFO7/N31xXWVDJiHi435aOxVwCopiLQoE11dKFN/b1kgJ/zRWLCzk/InPlJgZmrvPXRS/nRcaPzvwKvISnnnjBZdMX4e9CBS51h2QYT0O6Wf6Oyu/OTgpkBI5zzcagB0RukJxpKf7nfnLE28TG12fe+cHI8tA082IlnXxN+tutfGcRrflHeqLxm3v0o/Trq4C8x2Rncau+ToMECYnImT0+Wsr9px+q+Q4QPH/n104tPwqQqWs49R891B9jRvHORPp6nM5y6wwPSPnKtX9KmDQMSWjwh2nPqROx/1ij6iXe17p8GYU1/iVrp3faZkh3V4Sezogj3JRkh+85cvX15tbNXYcg3Wa0j+/vvv9pCEN+3To323EudXZ+tcrW9nz062C9tO43bK15W8Re96ubh3c+sfajpDhEqnlDODQ35czju0VFhp4uwel7CTTKF///791SSrSVd5edokGb92MBwY8q4f2y25sc+ukzIjf5QpH7vqFqzrt7NB5UkFWDzWvQSOnSHdTcqTQFNykIrYD57goWxOyIBBQ/nEMfSOl+U/GR6JuCvkcXS/2XfK26ei0rrqTq49Pf08Ls48/tcM9tmUAMfrr5NrNw/MY1cmUWr73jQ5pA+6Rhz3NO/5TZ1b6TQ4PPask9iyVn62v9ooo8XYxnLMYz1YbZVuMpal0zs8AfT333+/+lvo7hg6606nVqmjimcbQtanvNeNla/5m+ufJyULd4unf/7555WOSu19/fo1jkFd37L2Jr1A2yDhUcnIurVzOrt2KKOO3Lav+ahBhzuUqx3p6Z6N8jT3LMtuXvHUHx95qHk+8dzJsbM5ff+jceBWuoppHX22fv0JxPlFPey5WZ/6B87aoC2nt4j/omXbaa2fY8T3sNJOMz5xfSc/h2t/93hS5TceGPuIT4m34oknGclzrePCROqjeuz2y5cvMejgtlx/5x+kNd5tptuXS21OlPJ1J6B3dVIf2Xbf2f6TbpzwIuVP5ZlG26HDxMl3SuPGTSXjleeeifn9EvG6n2TE+em2p7EqXmizuH9Om6gbjxNyfzh/Uh6X7dq0zez10vX7Sj9uDjxdNfCc/9bf9Z0UcDoFZPJjBpMi3xnZJ+nO4wFKCsOLksDjBeKdlZ1BZWco8c52kzHcGXyp3RND2tcdqBBcd324F73XQL0VyAww/p3KkLoj4LzHtARudZ1OwvG3aZof5uVW2e6cjClP57R+BP3Ktv4tRDmlMbtSR6ffKw8N/O6Ez9R2OmWb8jjwO+347nRzChRMPHZ1JKPU2NMFAXZtpLQUlPFGiF823uHjRLfMl4lv6kCmna5n83IrppzaHl2+HXZ0+DPVmcobV9bK74t0+/d6lGGn69+L6anNHV2RX+W/N58PmsnzI9ndzEuMoW53Odd/Yp8Xdb4Jr5OOtH43z1P/3b4DHBMmW4Z+vI5+Cnmyz3FyKirVOenILjhT/Jqmfqa0zuG/YnOerPnO7u7sgCs+2I6/3RzufLcTOpWRN9Wq7OTLJOyrT/dkz8TnVT9mWivv9S+TnerrW3zzyR4oupX3mx+16xbW5DTU9WSsT47CCT8nxhkH3OU6xdiVu0pd250yZpqV+FqvJ08qQ+oCACeKh7sp026025t4mNq7MqF3df1JNDkG6ZE45uvknca7G7vdUdPfRQ8j/L9BSRclRyBhSzK07RCs1R+xp4495c91mJdunXW8dveL7273Ljn9iT9jiI03PyrSBeROjcSUpzuFVeQ+7hycKe0jaOLrXvUnm4Pf763/lntrzY/c+boebThpJ+3ypzK7/r9XPidt/EosOnVsHnSdOluJ18Qc2+DdhkJd83OPcSwn2fUknt3PW23knUOfyFjMJ1qqD5QnMabjv+q56he4LrY1OdGn9vJafcDpKj5MeuVWrHGdO9012TcJg+6pl7o+dljAOcV8DCj5aSCfGPcJOOabxp3Bp/qdrjtKuO51sxu3bhx2AaNdvcm2nOy9W+bAceCp61hyCNK9rlz6nQxyn1Raa71R6FMgZ+pPutcZekz38VR+7wbUYJYCSPVJR1Uth2lXwbxb0dd3B2q1KOm48N5JECrJuPvdyT6ddkrlrgLU76IdqCXA7/I43wmIprVZ35ODwTKdktutv1O6qtAf9O+lbk5Z70/lqb92jy1YH94yn41f6X6qz5szKc+kFzrM4xpKp3+J5clhSHWfyoNYMp1s8bh0x+xJk23xHjK+Ubfdgnu3UId1/vAxuytEZ5BpSbfTTujyut702Ahxxm2kuvmZnKNb6QRnfiUWXXUYH/R+Mr5MutW6kb/Xevu6BGOK60v22dRuV4evuzxXyet2WgfEkOS/UWZMYxBvrfUGq99DySeaZN6Nw0na7vfEY6JbMObED5jaTz4t5499sdO5lXyaTl5pDGgLEF+IX1x7DC51p/f4OO3T09OPDRO2XXV2GyTdGpgoyTjdu4J56XfCcfPK6zQuibe6vkWnvOsdT2khk5LArIi6aHenYKmgqozLUfFNbfN3Ok3SKXIDlJVspdt5sIzSyZJuEtT9FIBzerejX22xbbaTji5ariQ7Ih5HO2xJtr52WlJqTvN1V+dnorS4OUaUaTLc/bico/8s63Y4H6psPUJX33SupnGs9MlQvmo0T8bNr3D0HvR7yfOou9/NE+v/hFM7QE/t+J4xoK6NL13fOkNjciDIk3VI6mMRZVIbGcmAMhakQF068bpb38ZF8uSACWVXPJBS4KNzFO5B1rk7Q66ro+gWXjs8TNiY0juePI7VT8qf9U59Sw4EDf213r4zx7aX8Su93HVyoD4KZ34VFk24+cC+j6NuTaZ1Zz/i27dvr/7K3ZvRyT5N7aR79CGYxpNVbJN9SJsnTL+iu9LaPrHhu9NOKUDHPPQ73kOJR/uWSY9069Cy6E5PvWetEmvuUV/XxuR31XU3/52WyndkfHF/01qcbJ108inZNGwrBaO6009pbSXs3Mkg2UuT75p0xgnmnWDGbj5Ntl2nq65g713f8TQZySmNxkMpWDsFNHJtbCTDP7XVGTdui4rSyjDdYx1pIiXlxf6wrgIwK0Tm9X0b4OkF6MxDZe+0tV6/QNyLlelp7B3kuGI8eYGxDMfB8t4pxs9K06KfFFoqP42P05if9Ux5E7+cjx1/nCO+PqVpHj3ov0PWN8np9H3qBTu5a7013pPO8bxN+OZ53ZUhxiT+fKLVfFzVbcl4Kb6mtWt5Uq7do3e3kPV6tUWs9yN3lYdlOsP/MxP7NzlWE85191Oe1EZnrJtPz4POCLXOT7jkoKV3nu1M0IbxGp3w4CpOnNT7K7Gos3Ef9HGU9EyR15mD8p4PXpMpeNTV39XdYUnVk556cD7rhXuR+15kPe33933//j3+ezZldC+sSbyl9xeSkv26o24OXaGdv3SV3I/U5xMceXl5+0Lx2vzubJPOj6x7ydcmr1dkkPwa2n3daSmWoT3x9PT0Y93ahuRc7vyb3Rqb1uOtdh7rTXierp3X5dyGffFb6PKjdha075nRyUFgedbrHeNpUFJQKQnEitt1GSgmR4YD2wWjDAjkNxl5a603BhZ3ANLR3uoLF4sXMOvzQvNCZWCLSiEZkxwfyoEyTXPFNC1UKiSPg8diUn6fkTqjvH6nPB6T7vRBZ+zzfgrEpnYn58h13sM4T/riYYT/dyjpgA4L+Ntgmwzw5CA4AMXgih3ztfbvIDRIG6uSo2B+u7ROFybHwnlSGk/ZWoeUkUadRHy4h0NAvUK5EtOmdow7lXZPsuxvxZtkc1zlIV0XkbepnYQpXVpXJtU7rbPUVueM+LEHlmc907w+oROcYd27PPcg9zfZzQ/sux+lOW59b7LOPQ3E7+Yn9Uk5td1TBsa95KtMOMJ+nJCxleUnvKvfpef9V/fe+OGmULfOr/BcNPmEpk53JfLGSNJXv4O6QwBFJ3Kd8GbCwpPx6jCA88N8dL878hNMHebZ96qy3RNIrKPWhHHh6rzt1qrJ657lSUlvuD3iSeKf7XX6JNHpvL984ikpIIOijX//rjzpOD/zd0Ebt+02XK4zUtyWXyg2TXqWTyCQ+p4cc3/KwKeM6rrueXLbeeqAsJu0nGg8FdXJe2c8FqUxPeVrly8tvJPF+xnodHFaMU6G6VpvA5eVrzvG2hm33VhOYDbxddVonpyCB/13aYc9nV5IeZMRzXuTzrLud9Ck0rp/WE11n2CN73VYNcnC6d6Frj54g4OBqFvWY2f8GidtMBJzqtxHBpo63pP83oMx1f/u9DHbnq7TOHdG6+QY0RG0rM2Xx/9EVydbgTw44OT5kAz8RFfG47Pjymfn708nz8Nky3aB/rRJOFFalwkDvOlxYlfXd8KcHS7u6IrNnervZFaf799fv3ydfbvX3Lfv1d1PeXe2QOXZXV/VS9PvW2mnTzzH/d09dcJ8t/KV9HuSe3pq5x5E/tMpKB/+II/MO/lFbGv6JJ7S2E1rmphp2U58ub0dn52O2tG7Ru90siUlkn6nSKfT18ovj94ZYJ0im9LZ/q6vkzHKBev85sP17JR9uia/nTKZlIzzTLJM993v1N9d+km+eym+X0knCtPOVbrv7wRWJ2DJ4NaUb8fvvelXOJQP+rx0anju9PeUflr/rfnJ38SPjZ5dm1MbO6ztjMadgXGKpxO53nI6vn379uPz/Px8bJh1bdybkg1yj3as466enNiN80n799CznY1ywgPfpfE7aArGXcnzEfw86ONpsqE66ub1FMDqylK3JBve+jKVcX1XfZ2JplOeE9ac6O2uzlt5PSXK2GmnxMe2ij5CT/zOeu6NuZPfkgK4p0Hd6XH1jse0rq5Q93jort0T2tlw/u17u5PZLy8vkW/LO9VRNhvrPJGD6a7veOK97toMuh4+25zqdP6Xl58vS03Ru66etKNQwqxIvA2iamutnztxKfrJiexdQ+5IcEAdkSR/fgSuvhnJ9KNTjtq6//zN6DL75OddvSvhRyKtNMgHKb2Xg3yl948kYEpy9Hh0i/iKQpjA1sZAyss5++3bt1fjRNlZrt2jjmu9/avqyl/zJc1/ysj3yQPH30dOTx69SyevdoZFt3YnBZvaT2PzXnqPAfRRxtO/lbo5wfvdbljK65M7a73WL1dOinpXizwQtzoeE4inHd503+0mPe183XWdcvK/5vmkLXVJ5S2d4Hcm7MjjSn1n/C+5sQ1jkPXVlUcAT9YksfwqfuwMMc+jasf1d7qSaWUIFp8ngcsU8KK9kfi2bq26ibO0R6b2au4l7Nvx4N/JceHaSLJ0fYln571Kt5bt7Juu3ns5pv9Fsm3GdNuyk37mfPvyZe9SeU7axuf1ly9f2vWQ5oKdRvsixMRkHxMLuC4nnZd0DnGResFr03lsX1JHdHSyBrzu3U/esx6jP+i5scObnR7o9E5XjvxxvrhvuzY7fOl0ZY2JxzrZ3BN2Fe/lv/A1NZ2PkdZo2ggyDlle0zwhfpo6nOF9y8trMZHr6N67mcbDvPBesqumdrs8Xb86uoJFx4EnO5E2rCflmBS7GU1Gug3SKy837Y6d+jfbcf92Ze04sCzrnvrmBWPZeAFTeVvmfkyhC56xbk9aK9uqt5MF66MST4uVfKV6ea9rq1NoKS39dp076vJ27e/ymAwkHn/f6xyvNGdP+5TK0RnpHrfo+kPl3zksu7JTvq7907In/XC9t5R70PvpFFMmqvw80k9DkmSjnDjSGWU2st0286Vg0qTTUl9SmcngnOokjtnApiHGtUxZXtGliVgn+5TeR5g+a61XwScHGo27qW3eT7LtHjHocNNt7GgXdJru+XSzacdPZ79NGwwTdfZUohNZ7RyGVO4WnL9CEz/vafPE7jzh6aP6/W+jTm8mO8T6yfeTL3DSPuvv6vDYJr7TvDmxRyf7uNv4cHunesK+i7GGcrzHI1Xmd+p7wgLinJ31sgs6W9qyvFVHdXjjOrt2unoSzqW5kLBpqr+jhN1+jDrVyetd31Kb9yL6QdyQ533bJTtbkGOWxi/dd3up/q79Li3dn3hN+ac2E11+uTh/J6NxmkC7xXdiQNd9Lnw6Cs4/1ZWMZwPKZAzsAIHtdyA2tTUZnd5htwyLCB7Oa4B1m3bSWIZ5UtvdAmRaB1ip30zvlOjkIHR1fCQl+fp+fXcOgPNTAbpc9Sc5DQ5Q3dL35Ki7Lyf17oymBz3oFkrGFO8lHZzyEBuSrvbaSzqUGNXx2unpuu/6bAzaaO/6mtplOf5OeJJk6zInNNkMCVPsjFFW1X+WKfJLX3dGa+pffXeYwvITplzRs13Zrt2OLE+/IzA5UvWdAn5T3Vf7ckt/biXj70fj/b3oFj6TTfqgmZKcu/ndyfdKcJb6hOtxp58mG51pV8fe+q5Lsw62jE6eapiwlnhJG9OYNPWvc87Zl+THTRhs4ilgnrA9xZodGXdO8Oakzg67uvtd/d182WGRf08+TuK/yl2V7T30fQoyFT/8Lt5oM3rNTrbPVazd+Yn8Zv5uPVba9Nv1m48rY3Pzo3Ykg/tOiRaVYVknmboX4031nQaFJieA5aZ6K09SkN2RfB/RTA5G2n1PxjUVg5Vqp1zY5hWF5cVDfspATfLbGbeOciej0Hwmw96KeZLD6YIyXVVcJ04V+59kVt8lYyulE4WTfp/2JY3ZlK+L/k9K609xAB70OWmal50eoJ7xhkNncCYDywbzjqcJVyZjMOUhjjE44JNWnf6cMM1yMS4xjQGKWw3BEx2Qdr9TMCEZdcUjDUFTh6HVtu+lR9pONjtSf90O0zs8m+R5Ssbd6Td1/MkJN+Na9/0e6hwZr8UOiz87WUZT0Cz154Gt57STFfVMctg6mz61Ydu+C7Ds/BTWazwxz5MNnPjzGjdf6VGzjnwiKOHYWm83T7r0zpknJR90x6/1RvLPkj7kfY5X0pE72VM+OzxJdZ+u+QlXpjZSH3Z8JBzpfrue6amXKuf0SV7vwR/7Yly33qThhuTJnDNG2TdM857z0XyyzC441M2DzpZk2SSfKU+iuwSeEnULPgnd92vylYHtAEVSht3OaDKEyN80Ma0QrXiprFkXj8tWenpsyYqZdfl9P2zDjxOkv6FkHVXPWmfvc2C7KZ1tdMEO11+ySP+iZJmnMUjX3D1KgagEdpNSZZ5blJTlkBRmUshrrTeKbaesUz62c0Ld4xTTyaapXNdXr5m6z/XBcu+VfdVzQvdo60G/nq4Y3AZWYoaDK5WHhgTbNIa4/RPdMRmi1kHWX6VDU0CGdXfGmY0K6lEHGBK+MRiUHjWgrLo+GpOpw6ZgewpApfcZPD09vQrOTe+isnymQJDHpQLuxJ+uHrfV/Z7aYxnznbAmEfUyT4z7XRtXnMyk3y2H1LeUfkKcFxN27HTEn0SnePagc+J8r9+TU1fXzNvV6fmccGet9WbdVR2pXuNbchKT3T4FgTo5JP1PnUb8SfwWTlVQhg46y9fH6cTfq2v1tExnf7qf1onsC+Xm009sJ7Xt69Og05VTdqm+q210eSa9TZyxH9r5NHXNpzOMd2lsEzZ6g+hWfZ/sP/tcTO8OiuzsAdpYLpv0j8tQdyVf0DI+eY/yiU/ZtbejS4GnBPKd8rWwnJaUXn07yMPF4GBUCvyYx2pnF5ziff42X+bRijI5LH5/Dwefiuvl5eWNgktBKPJiZWhFzr4yLS2WaczNuw36adJ5gbCurq1K81ypvnBRE9AnhdopoU4x3QJ6LmdjJZ0WoNJICnutny8UT/lS0DEZISzn+dj1xeNV5bo20pzpwMk65FaAeNB/l4wflWYDkdfW33XPp2qMT9Q56fFjG8vGlGTg+T6vbbBzDTK4QuqCQl6nlo8DUMWXdU5hVNIZ1lkdJnSGJOXX6R/jmPPYCEu4n3hJjhT/qIEYw02PK4Z5wp90f6qTvJCMPR2W2Pg3XtiuKVnbHjM/5sGOVArqWZ63ktfsZIs8MOZBa72eu7SB0vyx/rctS0zgnPaGs3WY62Z9XTsnNnNncyXd1Oks6gB+1nr9QuvO3qOfwHu8pozsu/hkb2enJjq1KZMuJpZ4Y8r6t/povWN92z0u7rSE/6fYkvrWjXNqyzq6mxNd3UXGYNoR3WtC/JvfCW+6flY7aaxOZHYreX3aHkm8e53av2L5ya/s1sQk304/mL+uzlTvFbne/R1PZo55uzQGbGj4Fzm9G4y0ALzorUio9F1XcjZ2u8NrrfgyWgelaOBVXuYp/q0MUpCh66/r6d7fQB52R4ZtqJIvyy0pE9ezo6Tsqm+7HYIUnHK9SaklJXtC3Tpwmsee6TtlwXq7dJalvHfk4Ohpn7u0TpGu9TD+H3RfmvR20iHW2yebCMYpfnd/ZJGMV/JJnlzPxJ8NG6+r7h9S/vrrr1e62rjF4H3nNDngTH6Ij+bNeqsbR9sGzG+9WDLjpg2dAeraSad1uj8Z/nQqbzVqU76Eaayb5VKQrKOEFyWTGvOSnedcMlAnHZ74S+vviqzcdmesd2uB/U/3PwslnDRNDsNn689nJ9t+tps8BsaVKvf8/Pzjvk9BekO41tjz8/OPf6xz4CIFKniPcyD5RsSR1A/rtiSPohR4urJ+6M/U7yIG6VIbk40+2Z0n5LVmu6DS0+n+NC8S78yXgu6+7oJBnU9zZb13epjXxpT0na4TJez1P5mu9drnSHZF6kfXt47X91Aap+6b1Pm7lPGky9+LUbu1kHTCCT87f/SU7v6Op/Sb6UVe7EUOnFjReYfY95y/a8dpnEQd76mPVgwcPPNLqnx2YswfJy77mHjuyvg6tbWb6E43WKQgVJqkbnM3aZPCrPYt5065dkZw17dbyIt2Mtg7Rywpa4Jf50R1ALnjd/pt+uuv/C6nru6u7ztZT8rvKp0oxHu19aDPQUnfWSc4mJQ2IvztOovKyHeeDkeqjOvqdPiEMzbQWVd38oh623WyLcqlylPP+FE/tpHoxFHojMo0Jjbu01hZdxpvPEfq23OF6d+/f3+zo0peWSbdY1udvp5watLxnQGcdPHu43z8faozPbeY1vF7Sgm/Uj+7tHvizEdQwnimf4QN818j4kBHyW7x/KXOrN8+8WTdyic30jud0vr3Pes/63CX2dVFbPQ6tb9whUoe9BMS/jiItsPJtD54PfXZZVw/ZVNU41l4z7yFNUlndu37t3VkwvVU1zQeXT0TzqV6U/mu/ur3DkdSGjdE+C/pu3nd4cxOfjvi3OT3WmeB20RXcdT8VB3d/bQ+0r20Hk/4SrYU6zihy4GnXcdP81kwu9+O4vP3tEOQ0rpTUMw3LcDUNy/i7mi6lXqqw22lflhBJmWZlIH7bGO9o258DL5VN4mOCRVNKuvfU3+m+1cX9S1KIFG3+CawpBy6Re26pnu7tUk6CVil8blCXflTRfegB+3IOnCtt8ZthwdX2qjv1J6v2Ubn5EyGY2qjw6KpTuueCdusezsjnHLtjJ2EEUkGHQZ2mMAgvPUXeagy3bs3klPTGas7Waey7kP63bWT+LoadDJ586BzDBJdWScejx11dsOOJqytepOd8adhTrdm/qQ+fFY6sUFP6ign2e9xmvSCN9FP2klzIZ247cqn65M2p/xpve2wLunNSY9OvJ3wddK3lN+ntZ6efj4+WOWnoEpXd4clk2yYbyejHVZNdEX+O7I8Jh8nBaF84nmSm/n3aeFbaNoc8nU3XhMenfap42Fam7bf7HvaVkplEqXxc592dNOJpy4Q4ndeJKFML9/iYq3ftci50OuldemeDeG11itQIH/OSzDwb/NbvxmV9066+5wGlW0k45r3/chF9aXS/DefbKPa9bs5nIdyMnkx+fSVJ1+ShY8AnyjPBATTiSdG73ePJHSKqVuIHc+WjRd6/eYpAUfMq2znEPjaL4elfNiHuu4eD2U03305PUnFtk4VftIT7wGJiRJQfGR7Xf0f3d6fTh1wcT6lx1SZJ+mkyejyCZ7EB9evcYaBkKqviPo2GZC+Nh5a19u5od52n9gX6o2EcdRNVd5YQIz2ey8SNkzvVdo5JszXrSH2399rrVdymgxe1um2/C4T6sq65gko4pMDMElenQxd7mRzoPJzLNnfhBVJFsQK4rXvJXl1p72Mz7v+8rfv7+TQ4fmfqnv/VL7/BLL9a32abLC0hmtN1GN3a/0M9FIf8hRHR8QM2otctz75Tp46/Wt7sMpRPzKN/aWMXBfl2LVXdU4b/KbSrclfSTx0vHRO8lQu+S7EPo7Lbr50lHyE+rZvw0c4E17wdzopxjyp/pRe/O/Gif3sHk207IzHlJnnNuelA7Vct8blrs+pHzWO3sziZo1PPXUyI8/8XXmrPtbl8qc6P/XTumqaj5Z5moPOm/QB58sJXQ48pcmQHHNO/N3ic/76XQEmGsbJwKq8FgJ5piC7RekjsO4P86613oBJMsIMAu4rnZ4duDndDgTb7ABlB05Jfl1al2eaByeL62Rn1wufi5jGf8rb1Xt1x3WinWNF561ziHg/KWMrcyv9Kbi1VgaxU4V3lX6nQ3CLQr+FOv3zK9r+t9AJcE3y3QUVWNaBlsQD9WxXL/GHupd1pcepU33UZcmYS++A2hm6zFMGljHVMqLhlTA6lSNNTsE0Rl3+hNfsl/WocTs9BnFCfry4ZOOAfTLaOp5dpsOlro7pt2VlY59pnBPVlh8b9WPenhcJXzpKRrbnw8mmRSeznf69Sqfz5Fe396CPod1aqjy2w6lH63qtt7q6/Isr8+Xl5eWVDureu9bxyL50eZMd3fkH1A0ndSeezDs33u2rnPgM1v8su9ZbmZ3UOdnDaayTLW9/LsmLfCb86HBll3dXH3HcebryuzFI9oEDG5YPD44Yd9ge60vYYZkmrGVd7Dsf79vZJ53cLIuO0piQjy7/RNO4mRevKd8/9cemNk7o5nc8ecGcCNvG4FSOAkoL1RO7iLt0bDvxk+53g7UbDOdxH5MSZb5d3qRwUroVb1K2J+PVjVXXR9fbtX9LcGenDDu5TPVdWVy7+taaF6xB2jJMzhIDmklZp3wvLy9vAlTmIzkSE38fSSdtvZefK2P4oD+Tkt7sDFh+dsY4sSQF8RNOdLTTe6lN989tdDo2GRaTjk79SfhS/NGQv2p4dfcS3p0GEipvMvrXeruDWmlXDKZOHie8Xa17IuN4lycZlE9PTz9OijPNGxdM6+o/7dsJneLxr6BfiX8P+v00BQVS3kTGF66lpDsnTKp8vJfsNLd9ZRN5l57WQMIT64FJh3ft8Lf1QIdZiWzD+vtk7FJ/pzZZngcfvMmx8/sSRte3Nzl2vPgk6hV/KNVvOaR6PCcmuduH8emntd4GqVJ7t+poljt9mmNHyW7q2r4Htuzslp0desrvLXRa3+V/tZsWcNFJHivetV6f/qnoo4+V2vDtvslLN+BUaJ3B798+fp4UaHJ+dnV6Rzwdl2RepnEBs9+UnduY5Ol+dNTJdMp7MjH9KI3LUcFSBkmBTopz4n+Xj4Een25IdVoZuw91Lz0CY2WejquW3NZa7Vi7rQQQE0jbcTs5Nt5Rt06v5plot+4e9PnodFxOnYDOUPaurtdwWjuTMdcFcLo0UtLp9d3VV23u1kzij7q/c36Y1+mUVYdJVT8xdadjyoB3PxNZ9l3d7Cfvd47c1E797j7d7vFVDCUlnDkhOzzEFWPDy8vLj3Hii3NfXn4+vp82KyzrW/i8lzH+XqKs3uPYPOjPp85hPiGuq7XWm83DdNLU9rudcmOa9Uvxx7QOMxJNa/AW+8tY09WZ7PHJzi/907XnNOp856sxSH0jD/Yzkj1u+z4FAfnbfLB+UvWfgSfeew/+7J4GSXZPR8n/5atwOJd5wqmu/di+5Uc7gpsm5C2Nd9fnW6iTQef7s8y0viZ/a9ded/8EU7s1k2zaK+2f0k0vF5+M7JTP6STXVZOuJi0VRFLerKebuKltB3YsdC+ENKEMBilfFyhikIF18pgu6+QE9e57WoR+TLHrU/cIoMdnytNRyjc9alKUHm/wNcc+PbbQBab8Oy3S0/7ZKZgCNnYAKj0dx6ZiJjHg6WedS2ZW7J5rrt9zoeuHwfJWwzyBmcGb8tjluYVu5f29dC+l/aBrRJ1B/KCOXOutwW984drwI0nVDvXJhEPURWneG08SttEQnupjuvVP0psTxlKek54gX8lYTwaXeU/GsPOz3WTQp7bo4O0czA57uh3ohC33xhmnk+80Nsk4r9/pMTx+HKSaTkZ1fLK9jn6HTk4y6+zBB/37aGdTdLpjh+MvLy+vsOX79/+9+ynpgMKh+q7ydY+B4LROO9/iFkr2c7L/T+pJWMO+2UdiniRz670dX11dpPSIl30E8spr1sv27aOmcm7fba/1OjBk2mFLSt/lM5+JrsyF5BcZW4wxUz7KzHlLXq7rZK2mueRx6GTS2Vn+nsbgPZT0163jOenCj/BbbnrUjgx2hnMC8ZTGj+v0rnSqj8Y5J5wN9ORQdwtxrdeBKSsk1s2J79NKa719twfvWeGmgECluyzTO4OywC+NUTpVdBIUutV4vpW6RdQtaMpt5wR4vN3uFYXTjZHLeT55bnDOpufrJ8XC4FP95nXicwfQad6keXvLboKNkVvz7OhUET/ozyGO4wTAHm9iioP76SQPscQ4YF46nDK2df3peDZG7k7rTPjMfqWTL25vMvhNbHMyTCejMOFS1cnvteYdzArST/qNPPqbeTpjncGnjpfkAKRNkUSdfLrgk39X/ywDOgUs1zkAdH7Nx4SbDFKxPvatk/uvpGSXPui/QWn9+H6nrzodV/WV/fX8/Ly+fPnpalVdz8/Pr2woX5cNX+QglPm1Lreu8r2pTNWd2nE/T7HG+JKubZd6DMqnmajaK+KjbyTKf9Lf/Ga6+9rJoesL26RdPpHHNJ2I6q6nPu3WgCnZWimQ4X6zLGXBJyj8IYbQDrP8qj0/NUK/eufvJHkkmjbpJ1sl1XsL9rEfrtNpnX2wa7+zxe5BNz9ql4zp5AA4b8qfjDvXN50E8gSjwZNOFSVektJlunevrbDpoLBuKoC1XoOK2985QFXedVqZMS+DD1ZanoQOqCV5WW5X6bRct3Dqdxq37pRTl/89/BVZNglAOP/tmHYKuU4upX6n6L6DTXQ6kvL2/CPPbN+nz7p+n9CJUX8vwz+N8SnQPuj30cnYd/jC70rvdIeNd+tEbzz4JNBabx9tTQ4sP8QUBr5S/7u+kO+kH3Zz2WuAfXJbCY+MM9yl97H5RMnoTH2f9P9ab/Ur+06+0uZMcjY6PbzDFI5Hdz9RSp/mPuVkOyQ5Cm472SbdyVjaT1++fFnPz8+vAnmUd1qLqd3uX4h3DsDvpHth0YP+LOoCBafOas35cqRrDVkX0WegnU4eWI/xim0lv2xHzpts5SKfsp3IfuGOB2Na6SHjEAMUyYZNY/T333+3Y/fPP/+0/Z345fXOFnGZbj6lgFPCkIQ13AAxnxMeJXtlN8c7X7DDbtsVNYbGlbrfycFzPs3dIscBiGmcW1Nf2Y9dMDBt2Ezy9zy7ijPvwUvPS8/VX0V3fdRu1xFPcqfTEK5JYsMq1eGgk0/5dEZe5U08pHI2cFMeOyHO72DY5ICwfDdxHXhIZdnGZAy7fSumq3RFoU/lUx12CCbF3f12XTt+J4XRzVFTciDK+Cf4ct7YWbOjUH1wIDQp2O67rrvTS/dSTNQPBqiSxUmeqzTNpV9Jn9XJ+pNoAsod9nQ7vF3QyRscnaHp3zs+fE3yjmW3w+uNgqnOdD8ZjA5a8NRsCl4Yh9d6+wgDedg5cnY4Jv3aXZc+JU9uo+OLbSWDsMOQztBMebrfXbpxxjo7lUsnl9JmXcmKPJexXnLyiae0aVXts13yzDa6k1W/mpLjVdfk/0H/bprstFvmZsKQqsunVLp/7mawybZ7h1vV9nsp6Vzr9luJ+Ot1RoxJQbbKY11mqjQGGrq8HB8H+CyDxEfCH7eT9PeEgebPeLLDmCtzIfE6/e549Ddxhulfvnx59W7Y5+fnV/UYh5PvW2OU8I5j7SAl73d95HyYKNktu+BhwnXK7YSM/cavU3012dBux+WmOk/p3Y/a7Zi4RWnbCV3r9SMSNhwr784hSUGezoicFi8nP0EhOSCsw32ptMkBsRPjCezFnRZ7pyBP5DbJybRrp/p0Uk9qs6vbQSjT5Czs+tXd3wVHbFyn9ZCcCSvdBFqs2/OoAzaWq/zTcVFSp1Td51NKMpucrS7PLfRwJv49dIIxCZhtCBBTumv+ptGfTtQat2xoX+nfrkwKPnUycB733/rQ+q3DmRSQStQZfB3f1G/uQ9JJHSa4/k6X2UE5cQh22MO8U9tXcOZkHn358uVH/vS+TGLFLvDkza3iheNebRmb/Dth0e+kZLw/6L9Bu/G+xTEr4npyEIof+iPJZrc9v1b2C1y+8iVbf+J1hzmd/2UeJhmlfnWYOflW09glXd7xQx1mezjZ72m8mD/Zssmu56cjj41tl10+3jN1vkvq/0TpiRrWv8PICj4ZJ1LwlieZ2T/+7g6hnMyFHbHcFbvr6lhc5ck6o8vne8w/2Rr3xsibAk9XaGeE2oBLxqaFyiAUd4O5W+C/wa6y03uP7MzbafCkcR/SgHvAUt+SomKbyeFhPZRdfdtYLFn5aC75TDLlvR11C833Un73v8vTGfDTIr/Sttt/D6XADucDx4f3OXbkk89A+zGJFIjyCahk8BtkEkCyP9N43kq7OpNi7dbPjq8ObO9B95TJf5VOZJjAr9OhTqu81KXePPC1HxHi7i93aPltTOj0UOrbzrBPuHUS3OqcldL7PqLObwcRqr6u35Zv8Wm9Yn1D3dSdWuqM7i5PNw+cltq2DqTeYRBnMvpPKBl7XZ7pfvc7vePP/85bHwaealx9epY4nJxE4xzbcWCKfKUTFdNYThg/Gcnm8WScruj3exnoD/o42tkB0/jZHnEZ+hC2410PdaptLK6ZdC/lS77GpBumviXqNhompzbhq3lnPfTxzLM3hrrTT9QxCXOSLVn1sg7rn8nmTHOKciD++TP5Kbt2Oz5SndOmTJc+zRdvUEy2eZr//EfVtBmRArTEXvez8vrJEJ58msg+Uvc7tX1q592KDZ4rnbxPsG9a5ynewXvvpXcHniZD6NTQrrxUIDbumT85olVHBZYYYGI7dfyO7aRTUGlQup2/upcWla8NUgYktutF+PT08wiujfnqmxfl33//vf766683C5dKwM5Ex/+UZ1KCXb5p55nUKaOuvR047+algzHdIt85Amu9/vcrzm8qxqo7KZETOZrn5IRyvnu8a04bGOhsTnRFiSbjpOtX5yB2YziNrWVxhXc7T6fl3NaD7kPpxKjHtJs7dZ/BehvtpWMLK9Zar4Lx3l1z4L/aSHN7d3LQBrSNZs9xOjgm78jZeUk4U2l+0ee3b99+6AfjDOWQTrgUz92LYY1fdtosu6QTTw0+tudrt02eTOSljND0Sfx2abY3dphc/CUyTqXTrvX+k0rjeH3//r/3O1XfiBOp3nS64q+//nr1Al+m892BDlwmJ9LjvxvvHTbfavinst184vVnON31oExJ766V7QnbAZVGn8F1Jb1X8/n79+9v/JG0XtfK73RNturkMJ7YI1dtFurKyb4z7ibdTh2Q3i9K3C15sa/+l1r7ax2WWs93Pic/nY9K8olQjx0/pzqCfBN7fI98mueJEu64P+keH8mvcUi2mcsXb/UYntureriOuj6XTPgyf9+fAlDEOWJS2gQj9rFvri/Ja4dh3RrsNotO276Ch53u63Dtit64y4mnCeC7PGkCVvrOYEj3UroNoa7MzghxPTZQORlKgXiBsi3msXGaHFzXkXivBW+Dy4oy7UoXue0kz072V36X3Caa7nf1d+O4u7+jJH/+7sbIlJw+85bGyu0xvds9TgBfv9PJhVOjZJLP76KTNfygfyd1a5K/1+oDnnXdbRykuZ0CLGm97nhK+ab7Vyn1KbV32jbxxcbvWm93LK1XzAtlwgBbMngmrE6ynfphHTzNnx0O7dqZDLquL1OdJ0EOt8FxoP1R/NkmsPPm+jwPiEnGJZbjuz06x3nCkoeef9Cvpp0ty2DRNHeJD8QPY0kFZXnisNopSjhjXT/phvS7W4+7/pxQZ+OeYiX1TMnP1PWho6T7p4DLThbTPQYxSCd+ksky7NJS/okoD/c1nf6dAiqct+SJ+psHQ6aN8rruNgqLKF9iF+2KdPJtCqR9JO1sGuY7rcvXRck/PfHjP5JuDjxNDvZ0344AlQrz0ECiYd8dweTE4wv71lpvDKsUsLHhlJRkx2MKDqSTVCTznJyAtV7/QwbLpslGxexFTPkxjfWVrDpQ4ZiZdkr0yqmZlDeNSyrr6yl/B2KnZPDq1kMCOQcJbaiv9fPZZysK8lz/DmHnovLZMWB9nPN2CEzp5OEkl86JvDe5jYdz8iAbO0zrjNr6/e3btx8nPdb6GRCh0+zdMOMHdf+p4XfSJ/Znl96Vnwx+YmvVZ4xI/7DEXWf239QZ+d2jENZJNJp28rxixHftrdXv8HLuWKbdb+pP82fDsKPOOezmRbVtR9dYkPq21v/++cnv1WB/jDWsg870y8vLK0ejO4VwC012yYMe1NFuziU90wWZuJ66wK11Zn37VCDt/Vq3tv+teybbs/tNx9t5TtZk5yfs/APLgt/paQfKK/Xdm0BTn3jSJcmm6vfmbMKe7qRTsvX5zZNYnR85kfWvrzts6nwdY2vRpKcTBhVv1Rbb5xxPQVX7SAlr6CMZS+vet2/ffrTNV8vUkz/2pcl/6n/y727BKvav89vfQ7dgoG3jol+Bo8eBJzt3ldZ12JOxEy4VSWrPg2PH2r9rUbu8I89MIw++zzrYZpp8PkXSPVrARZ52z/1Z6/VjdFYADjRV/kRp8SRDtDu1lWhyrjpldzrRu3zpuptDSQl3DthEVo7pfso/3XNwNB3vTH1mvnq0lMDK6H/aCUjzzIrWO2uTwvUuRyeHe1Gak7vxedDnp1vWJMtOeJQAlhjiDYvSg2u9Pc3D+e5rtl9tdP9M19GEN0mndPPezkGn9yqdgSXjTH38N8jUN9ZHdggol1R32uXf3ds5Ouk3qTMsO+Packw4498nQafUThrTNC7Om3jgGJRxXo9QfvnyJZZnvZXXdhHXFtut/Gu9/me8ciI4Tzqbyjyk/trWe9CDrpDnlDFisik455PdzrLeoKDNluzAKud/tUu61T4M+Tvtf1qDnf1adLKh3OGJ/aXED0+AmUf3edrsmXyqoukJlG5sk6zSPfLhwwRX7VWOccIY+zueEx1fRZ1/0M2Tzu4wJnjeGsuJUQ6qOfA0YQ0fs2PZCjxVEKrrS+cL+fDGVer8k26+uqzzWg6uz+V4PQWMT32+k/sdvftf7XZKOTGXFmgyHkpAXYBprfVKede9ouQE1HeaxDb2Pcjkj4DkXcSiZAglefnkU1rkVNKO2qfF48VhI9GU2rAz1RHr2y3Kk9NMJ/PF16kept+ijBOdOAZMN7/Ob0VGhZcCT3YgrChrzdQ3HwMtQ9/plAfr84516m/aQbAcrC8+gh5Ox3+X0tzc6QYbrTRySdwlm0A2OQzGBOOJHRHjW2ekdNjbYXHn1CRD3gYh1y+D2mXoWfckJ8AGZDKWip6enl69K4J8uA8nRuBka7BN4yPnR3KOpnHtcCbZE92Yuc0OdyybIhv1tneslz3vk1xtI5j/ZEvVdQVu6/tKwOnU+P0VOPOgfy9xTXW6IpUx/nj+F+1OT/qa940Jna5J7aY1WpQwqFtPJ05m52OkwJODL9QvtEGp57t33KQ+n4xd8UobgHLxoQBTwqCp3ZKPy+x8rCKWY1ClcLXSd37OdDAh2QT80P9MdaX5xvFm4I1tVb7yUYpv9pF1sr+VbtujsGYXTKu+WhblK3mNXw1AJT0x5TXt5ng353bruVtDp+v9Vrp84mkC+WS8uTzzsZ5kyK319tlovlSbp5W8M12Lm8rLLxz34vSJKBvSVSYZcenUEfm0nLiIK99a/d8RE3TIK/Ml8KDM2Va67wVLMOgUlcdqlyctkJPFc1JXV08a7/dStyg9lydnxfOuKDmjaS4kB9LKlHUVeactOQAJaE/7/Kvpd7f/oPvRyTie4IyxaWcQU/+mINNkaFD/dzrmdH52gJ8wNuXfGTfJIbE+IT4kh4H3kiyIxzb4eNoljaEN2s7oN087jPG9lMfzijJymyeG4M4R8m/rX7fZ5aGMi3zKzbvI3LhjO11bTPPp1po/tW5sh5QD9+XLlx99rrTJuHUgOK37h95/0HvovTbNpG+9/u1bcK1487rWU9XtdUV9nfwj17vW/A+U/iQZddiU+p3kR11BvV75ki3qzRv6cNa3lLN9QPNuX6iznynfpCN3PojxwnKs793GlttIY5XGcQqOpHGd/An6msxr35BtGgdoAyTbgNfcQPemVcKyChKx7kpnfxOlU4eU0+nY7GRdMuG8T7YP85K8iZWu09zzPOS679r6SLp84mln9HbOQPebE5O/3aYVs439Stvx4EVpZZ/upUnS9YU0TTamM3/aUbRDwGsGtSw77nrymuDjZ8k7edqRIO+Tcpsm8zTpT++dttEt4FM6Bdwpb5Ity6Wxr7xpx6d7fKfKeVc5AXoHshNRSVtpuq1u3r+XurrfCxCJfqVCftCe7oUza709wepAMHWtg1QsX9829Kf3/O36eCKD1O+Oug0EG6tJN7E863l6eorBAeNG3ePuZdILqR/UUad9nZyDjtK8skzMS2ojYTDv7fhJutR9Wut1wGkyrt0H2wJTG5WWxqnSbTvQieIJ2+LBj9hVEIr96gK9ycZ76OcH3UJpznc22kR2xFlft6Fd6yX9o53tJ+atdtKGdl2nD0+PFD/pVCr1U6dLbrGxjJ3GU+OK+0LiaVvKnTrI8racqlza2KfsmfeUUnvJZ3Ebp6ee0thOY76jznYnPhR//k3sKUqPPBaPtAEcWPV7NN2XZDvYvvAptV3QKa3ZJJud7HZ5drapfa80b93faX51bTi/5/jOh019u4Xe/ajdvfImZe9yNuw7Yz8ptY6vpAAmg4b3dn2r8kmhTn1Nxr7b6wAtOQ7ciaw0501KvuvfCRgnRduVPVXUu/anNkhXg06ndDLP0zzoDJ20k+CxTmPmcTawJJCdTj9NfZkes0t9uicl4+DhhPw3aQJ251vrbcCJ9wpTyhjy6Zy13p7ETViV5uO0XjteTzD0VoeAPCReyEM6iVlEPUAs9oupk5FsvZPkkYy3iToHchewmAy0NMb+fYpB3f3kBDB9rZ+bD3YOOtm4797AY101Xpzn/LYs7QCXnLxRkng9MeqvzGka2Q960BXiPLvigO3WHdcIbfFaa3UK0I8duU7abSf4wXTiE/Wt+WNZy+IEW1PfE48dD96kMdYQs9kvvjuos4+p8y3jZPvaLqY8TnE9fVuuruOEkt0yYTjrPgnAuP9rvd3kcJ6Ejd2Y1+N2qTz9W57u8dpgIKvaMq7t8DDJgCfe05Mo3nA/pcnu6GybbpxTH7xmmd7ZwWlO3GpDXqWbXy6ehHgPpncDQAXDfCnNEzM5CWmBWGF2g2S+Ko3EheRAQirXTRorPx4prH7xPuu1wu4Mf/42r10/UhDnRKFOeU4W15V66rpTyh11zgnvX6Hk0KTgIQGR1D3fbuM/gWgHrsXXVSP/tJ9O2wHkTuZTm7eUI723/IPeT53+fE99k27u1hDLck0RZ6xP0y5rUbdB4Pu7frg/t8jH7SeMXOu1/nGeneFDefCdDTw2b5wi2SlI/bdeS/wkmXUySfrZ9aRylmuX71S/JKdzrbeP1NkB2BnbHkNuHLD89+/ff7ygtf41lQEkOwTFi/vPXecdn6fz+ESGyT7t7II/ge6tEx/0P+p8jbVme2Kaw9P6859XdNdPT//7Awe+JJlr0xhT+rHaoe61bc98xjL7OZ1f1NEUwLIsiKmpPeZLfLB8OklG3LCP01HSUclGnvTX5H8w74kv01HC4WRvuN5u/Lp+8yRqfacXc1seae4UT1Vv2QT22dO7Iysv+1ZrIY35ySY6+bBsUn4/irfbcLccunGyfZLyVBpts1voM/lH7zrxtFNG3f2dcdRRGaw88ZSMZE7Up6enN7sJPt5qpz8ZifyHujQB0l+oFs9eFO5z+s1d4zISnb8WgPtAJcF6qQBIVK5U1lQMVF7d5J+Mu50BOE3qlG9yfAzCRSd8mxKgUF67cr5ObaVjpX/99dePv3X3898GHr403P3v2mRZjq2B1tcnuwo139fKzyRP8voMTkHi4WHs/156j/yp2z2XaaBWWq3B+k4OAo1fGiK1Xqu9yut3D9ohKD6rveKTbVC3J3mcOgdFVVfpjm7tJQOx2mO7ZSQWFfaWfmI6dYtPK7N/yZHhWLAu6kSe8jWunWATee3ypHbNY4dFp3YRHYDO0a08yUliH5OzwrrpaPzf//3fWmu9OpXhdjmXjK1//fXzfRucl2kNXl3bO1mmddA5ap5vzOe55fqu0C193LVzxcZ40Fuy/uJcuCLDNJe8/qhjOz1eZSroxIBvegyH8zTpTvpLae5XO968JtakoD75NU2O8WSfUh6lh2gPJ5l6I8OPIpYs13qLFebVp1vYx25DiDhE+e3mjts5tY2rbDcPzHN3OqfrD2WfTtH+/fffPz4ej/SbfeNcrI2Nb9++vTpd9Pfff6+vX7+uv/76az0/P7869VT56BOx/vRaEdLff/+9vnz58spXIt5N8uAhD5/UmoJinA/JDrBPxfluf7bSHPBynzvM8hzr/DvzmCjVdZVuCjzdm2gopE7TgFzr9e5x/WZ5C7ETDA1m1lvEdrr6WVfx6uBYV24ydNPuAduwnOzI0Glxf1xv/S6gS4uxM3JSX06MtBNgPzX2praL3hMpJp0ssklWu/IGGQJfAmErQdeVlPCDHvSg/5F1akrvytHIKOOEBkrloWFv3Vtl63dyKtZ6rd+TY7TDp6n/rjc5NV05GzmdHirybicD68TMTo9PfTsp18mvoxMDzHlS0OkqdXo7GfpXThR1wZenp9eP2dW9chC8g54CHcng7egRHHnQZ6Bb9Ix1Zpff64Q40Tmra61XefgYGduhc1trt9Yx/RnyQryxvmce6//U39S/HfmEVcKbJA/6YNRtFaDoNjYsF8rqhDy2Jw4+86d7E536N2m8EqVg2rRxlbCDsnZAKm12sI1UNwOFE14Ym9wH951r5dTnOhmbFOTiWroHnfAx6Rnmuerv/S7f8MMCT5OSSopsCsx0+V2Oio3Ki0f2KlqblGzVwQHkfb8raa23/wpDYz29WDadiqry7lNyVHjN3+TVO/IGmoo4d4oryWa3M5nq6H5fyduVvZLnI4JOO+XWyYpBpDT/OJ85bjV2OyeViqe7Tv1KzqGvH/SgP5E6R9l56tu6hO954uMSNIg7fGOdKY3X3jxgWjK21nrr7F8JqhiL0g4pjXZSZ6AWVpjSu36MKYXNHdac2Awpb8LJUzldxY57YE3CkuTw+KSSnYRUl+edA4E+AbXW/zaknp6efnxzXtowd6D1xLC94pw96EH3Iq7VFFwgTTo+rTFuQLBelmO+9K9mZafXulvrp7/x/Pz8I/Cy1ttgVdqs3GGPMca41cnkhJKPZpyjnNZ6bSvblq30aZN8N6b+c56EE1N/3I5PT3f1eCyKTrGj84HIS3ffvKe+JJzhhzhd94nrSefXGJcNxTmXTtxWvQxS1dy2TeRxSP1I/TJ/5HGtn4/9rfX6JLtPnd1CnQ26G1MeuEl9ddpkFya5/Sq6a+ApGcSVvluAJAZ/qny3W1z11JHw1F7lq2ALy1ExW4FUvUU8UsnBTnV0fbMCJo/sb/WB/U5HTlkf20gLjcohAaNly2fMU90nZCfuSsDoSj0pz6TIryoML+rdtSntcBF8yZPBqOolUJbh4bVRYMxdoElBd/38HcroQQ+6JxF0mUY9W2RDmPfpGNS68js50kbH1Y8dkmq7eEnBH6/rW/Sad9pt2BMziqxTKK/k0PE3j7snZ6DbnEn1VVpy8siTy3XzYOrLFfIcOtGnyXC0jIi/HBs7Agw8pb7XN53AFMBKcy7tAPPbzpfz3OJsPehB96Yp8OTr+n3i9CXdwROeLlvXXgv1WBH/JYyP7BlruO5o3zuPTxJNus4Y815iP5NeI02Ba/svrI9BCh46SO0krHB9zmsb2fOiszeY1v3e4ffO7zG/6YCDrytfOjlbQaZ6EobvePIjdy5v4onwl5eXHwHV+lQQlYEfBmQpZ9pMaUx3lGy9hF8V6Kq0tNZOyfZeus8+dLaOfztvsps6use6viqHtT74UbvO8J8oKfdkxNl5r28HalKQxYGcpNCrDSp9GuhFSYnynicB+XbwqZNZCroZHMl79dH9NA+Vj31OMub9jjrlPY33TuGeKOQrSvtqvkSdgkuy6RynFEicHCErvAKAusfrBI4Gh7S7dtLfB72fHvL8PbTDIgd2fArIJ568hpjGTQsGBWj4c90n7OHmR/Gxc1yuBDmqz9QZKaBm/ZROek3y5Etyq43qb6eH2P/EL3lLzkJn7HcOgPXuLcb/1To6slPT3bOx76AR8b2bDx775IRxM4/Gt22ItWZHMbU9/X7Qgz6avAHN77pmYLajnW1q38R2dRH1noNLVYb+SHJiqX9qvfqECNu3XdltGpQs2B/nmchOddqgoa7aBWxS0IB+G/0g6sTd6yhSIKLqSYcLkqySTE7020lA4pS6cUkY0v12MIrX6SXjKXDFdmtcaAN9/fr1jY+d5q793NTGLdjbBZLMA6/fS904m3ePIdddyufvtIYSdX7nR9LdA09W4Ex3PpKVGe87QFPOdv2moU9DiffS7nT9JtCQf0408mSHIvXHvCeyAqZsuLgIRsxjRbjWT0XrXQ2Xtzzq2mWsPLqoNoGJaR3tAka7+7v6701JCXT3KIfJgZgM9jT2nCsu63xW0qzTbXR8PuhBfyoZSxIWdUaLdZ/1ZwrQsA3qXq9JtpXaZXt+ESaDP1W/aeckTfKyYU4dwjyTPq77xMgKPlXd3WmnqoN9NebteCB1jsvk0OzoPYGlnY7tsPzEQUgGf/o98ZTWw/Pz8w97y8b3rm47xslRXutx6ulBv4fsvCXd0vkknRNoSn5FlUmnORyQWWu9seccOOqc5i6IxXXID32lwgIGm4iBt9qLlIWDXNYVnU4pmaz19i/u68M/+Jh0HO1s1m8eOA8mR386hbXzgbvf3TzrdO+EexM2WBbGlLJDKuh0JfCU7iU+PU9rU90BKFOHMZ7jbsd49NE+5YnO6MZ5rdcnzW3PdPOgs3vuQbfK6+6P2nWG/k4ISZi+niYIyzva7clsALDi9fVaP9+aT6WZThmRTgxcK/S13gYiqGy7QFcy6oovA6b56sCkdqXdH/KQ+JwU6m6iWkHs6rlqxHbAMtFOsRdNASfPf5dLJxmS4rcRQkPCuzJ2eHf8s+0H3Z8esv31tMOiHc5UPmJIwhRvcCSjf9qttqPhQJMdGOYrujq3Ojza9YfvvGKb6YRSla0Axlrrx5F65qm+UUYnR8ZPHIvU7+QIJN6ne8lWuTfZMZjydY7EZOx39Xs8HARN768k2a7ayecRfHrQ7yKv48lxZ1rnlyRH0PixO8HqNNt2VUcKMJm35Gjbl/D6TDre+e6h97x5YzxImxS0e32IgFSPglUZ5ik89om2rk/GmJILMTIFA9x2J8/O17FdQErYz+/ES/3u8IJ1d7jqduyj0E9M1267+sX57H8K9OGMnazS9Q6Lku98L0pjbT7S3POcORkTt5t+J7z/lfTh/2o3GX9FO0WfHIS1Xr+ormvPoECDujtJ5PYSyHTKOPWj45P9T7sJycBNp1rIh5Wc/+2CZUypT+VoJJCaFHY34TsZpf5eyXOiKCb+bqUTZ+8kj40QljVA1HfameDOwmn9KQ+NnQc96N9AnVPR4UxR2pCw45B2ldkGv+s67W4nw8lORKUVb2nD40S/pf7aKeHmBXULHw+sNjtDKPHeGVDGlBQ47/JO92+lEx140sYJBkxljQO7/DyR3ZW142L75OXl50mnrj5vTrE8vxP9amP3QQ+6Sp3v4TwMRHT1OK9P6tDRLh3bvVd2rbfvjaq1yPrYJvmY0qt9+zYpcPJeHZuw1AGKhEcsn/g31vAF7aXX6pp29OQjFI+WAXnpcIiyu+Ib0Yc0dU/TTHiZfk/BjKkeB5X4bV/WQUDKqvpWNgUxh6e90ylpB5T4caCV62vCp2487HudbKjsaCrv9bbW2/eyJf5O2nqPPXIP+vB3PHWL+aTjnaPge3YIrLDSaSd/W0nbMUjKtqLm5quIvHu33NT1021Wv3gEMR2fZX18aewk9xTcWOvt7v1uYV5RrPfIczU48tFBp6uK/Gp7Nj7SOilKgP7RNM2RXTl+T+m/2mm5V3sPZ+s2mkDzHgbw1CYxIhkD5o9pPunk493GGjv/1b5P4BZ1jtDpWk9rjdhJ3ry7PO3AmjfKj4apT/Cm4AY/t4zxrtyVejuMuwfdMzhFjGCQyG35n53WWq/GiU5AzePkWLAsnaXkDCQ6Od22o4QPt2LRZ6ErjuqDbqMTm7joVqyh3kj+xVrrzTvv6s+Q/P41+jf1IUYZX0wn84k+lfthXLyFkk9j3ZGwhrKqsskuXmu9enn1Wmt9/fr1TV8m2/hXrrMrbRHfk91AOvU3mZ5OO01BNWJBegRvrfznStUXBpZ8stbB1Z1cqv50zXxprbCOjtIpxff4Ors0tlN5kg2S7MbTers6T/mbbPOOPizwZMXCiVAM1kS1obwLkFT9NGQ4YYs82VmWbVU9NrCpzH2csvjjbp8VIY9ydhOW+blzwT4QBNIEdKTZ7TNK73/IsIH2999/vznmWOlp95vUKXEu7qvG9cnC7hTTidFpxeSgm6/T7+IhlZnKWuGVEzC9AJHrJp1+q3omI59tkgfvEHz//j3uZldbnD+u032jnJMMOkMppXfO9xQMmAyxW6gD4YnXh8OwpxP9YKBMOHNa146XVEc6AUXnwHr+69evP9496Jdykp6fn1/tDK71eheb/eWHgQIbepMurt/klQ6R8cjGImVER6jKEHuKN+Ply8vLm39PZTDKOi/VwXJXNiHSHDLem6jLuvWc9NGpzkqnkJLu53UXqEv1p3nnfyeyPfT333//cIIps/QoTmHG8/Pzj2s6yGutV9fu+24MbQPVt+c00zrHK53i5v1Jl3T2wXtxJq3XaR7t6nlgTk/WO5P9xu+EOdQfXqtrvbYPrWNpm3c6l2TMoV1V6d2GY9JvtB1pW9q/qLa8gX6CNWk9+akN88RTL5S/ZU17t/grvVX1lg6j/Csvg+uUR8Jyfie9QZ1Cm3e3Di2fIgfckv7uToGl706XdDhR8uF7nXi/s7F2uEUMrXHyIQ730Qc97BPTJ2I99GU6TCqe6UtP45HmRflDXNO2E9PpPftFna1g2RYZ6xL2eQx4v5sX1Z/Op0tlT+nDH7Vb6zV4dkwmp7Srw4LowJrKk4qeioYg0BkvyRlOgFOUFBN5SpM6ARn57vJWHgaUmIeLs5wYy7ObPJxcqV2Xuceupds/oasnnv4E8phwDJNBYSC4RRk86H/UGQEP+txkJ+3KvbWyA5kA3oav73sHmvdSu8aayaFOeFB8JqNroo6fuqbhXUa676+Vg+40FJPTkd4dWNd0aGgIOm9nON+6fjvD71aa5tyvoIQFO+fDH6envG7DlOaw05MjcC+6tz5/4MO/g9Kc7daM04gDJ3XbLrd9f2J/O2j09PTzL+hp39P3KUc76UpilNthPmPOhBsdnWCNTzGRn6SX1sqnbBPW1r0qa1zqDkCYT153Pl/iJ5F9vkRXN1PeQ7sAR1cmlUv3E57Qr0l1TBhk8vqotHTd5Un9W+v1K2e84X8y/5N/f8U26MpcxaFd/s723NnGV+mXPGpX11fzWUl3zgDzOXJNY51Bn7pHRZ1OJXEHwnVVOgNca73+J73Eu+u0LGxkJ8XP+ktp+5E618XfnbPAo/dWxq6DNO3QsP+ndAJu/8aA01qvT+85eEgDo6MJQJJBZfq3yvUKPZyLP4dOMYK/STbsU14Hhewk15ql7q08tWbpXNf67QysFGBO2MO8xBbLwPJKciAZu5KT4vsk40t6pKs7pepHukrf8bQYyQ7Eqf7q1rjlf5U8H99joN1KNtQ755b3jTOeW/U7zVkTd7S5Frh+OgeRed+jg9P43kuvP/Dh30WdXTRtplI/J9zw+kiBC2OPNy2SXnf9xBSv+3rcjDq7/mW0frMfycG3X2U9Yf/HfO6IGFl96E692pY1JnUnVqhP0mkb2g30kcxn6pN1QadnO6zZ2SdT+feSx7rSdmVS2nTy1u15HNOHPnuqqyjJhfPiCo6kfOkE4C1jscPNrs4uOHlix03lujl3pW+32jZ3Dzy5U6eGF8ul4JN/W0n72B0NHCtXlrOTsNbbl/RZMfO62uGLAB3sIR+8RzDxUVwr3BQ0Iz09/TxiW/m8YGwIurzzV5vdwmfaBD4cjxOagk6U5678ew3X30UG1p2yTrs1rmu6JiXZ/YkyvIV2uudBn4dOcGYXhOoMyKRjqTd9DL90Lx+VsN6udOvo0pvcAKnyfvTCQQCfRjJeVLkkg7V6HWp972tjret3WvUvrSW+d8OOgf/hroJ19z5Z263zK8GnyfH8XcGnRB63tAaIK50TwaDgZEg78MQPx/oj+9s5clfLPvDh30VJfyUbd63eMTudW8mmNabQL/H7nqzX1/r5TigGaypfbYRwM9o+TV3XI94snzY9GdyhzZls00k+loPL2v9xPvNDGRG7ab+mE1v0g6zL2GYax6vO+U7fnNR3bww58QdOyux8jGkcGcSx3z35nok85snXneTMe8k/rm+ug1s2uaqu6jvtsc6/73jtbNjE+wlNPnPqw63tfPg7nnyd8pl2gvf9UtxUtJzQNvD9TxE2+td6HeX08TrmqW/yxPa7Y6pWrGlHofL5mdDd7mTJKLXNiVV9r2uWYzuV7sXH8Vjrp1PVgfgt9F817KiEuzWSnD87Daf0X5WzDcuHc/FnUYczKRCw1rzLZDDvDFmfKkqYYj1cxJNPJDoINviL6kSqjf60Y+3gWLVt/hMZi7j7XH21DFMgsMoTm5IRyce/Uz02THmfPNxC3Xq/su6N6Z+ZJsOR5HnEQFP6s5JUnwOJDjT5VJON3vecvO0M8KtjdIIPv5I++/z6k8iynObypA+oL4gVkxPH+6WnqfuTLb/WeqXXqVPp+/D0U+ls6nA74/YD7BATa4hf1A0d7Rx945vxwT5Fkjn74dMoxJaO+C4b9oV1sa3OHnef06nlSf+e6uaPIvsSvL6yVnbp7ifnVoctda8eLTWlAM6ELZWW7nmcOxy6BUd29zpemHeah0VpTU7zdpJTGjOWuWW+3jXwlIxGd4z3u/KV5ySax7JU/g6e+LRQUtg25KvN4tcnpaYdYwOIFaJlZJ55j4GoztFI971jTNDhTjiBifXYebCyIZ9uuwOjK5O0UxSnZa7m/5PISnsHBC73oNdk5fqnzov/Ap3gjB2BBJjMx3qm+0XGD+7UeWfZDgQN/8pHTGK6DUC/G4nOwFqvT866jg6v0lwnhpAvbvAkfGIaMYCbQ0kH+eRLshvYPuvu8u4oGVRXg04mz8XfqW/ZvsfD+Xh6jmQbw5/OKazvuvYLXXnqiXl3hvcJdWv/PTr9gQ//bZrGO83/LvDUBaeMAT5N6zbqZf8O7DPwVGnU4fRD2FbSq+ybbczOT0p4O8nRQS2nJ/+ts3W97qlb0qPe7qPT3L+uD5PN0LWTsK4rd+VkzVXq7AzeIx+8P1E3B5LsEi/GF/PC+owXOxyZYghTcCqV5WbYRB7jxGfKw/SO58pjv/wqnQS0dvyc0oeceLrF+LKhkMrZKGQbDKrY2E67B27Lp6a6OpPy5ffOELdiTMpzMhKnPCmgxPxF6cRUGYZsg8q/ylHuvHZa14dbg0F2+nblThfRZ6cTgyc5munT1Z8U7q7tfyMl3bLWf08OfwpNODMFnbrfdgJITHt5eXmjy6lj/cidd7O5I+0d0SI6AtanfOQsncwlz5MOONGRacfZxpb/iS/hZPX/r7/++vFoB3UPHYOdzTAFsK4Sx/5e1AU77sHvFR743fWTQSenE//TY3fd/LLT5+sujXx0jtvVcUpO4a3lPws+7PrwwKszmjBjImNO5xBPTwsQExwI4v1v376tL1++RPudVHq12ikM4glVP7lR7dK+J892fo01lU6MmGzNksuUbrygvIvHFOQpbLGcS66djUuqIF3RFKw6mSOJz26uWH/+Ckq8JJ/N89flT20oU7Kx6mPMqd9TXf6cjnviZa3Xm2HeGGOeHU3rwXIyvzu/rWurm2c7P6+zGxLevQcDf8mjdokmJ8ETd3IUfD+lW7BW7l1QiHmqbBo8BquqXQ6gTz7tiA7JdHKIeXz6qPJ0R1WTY0BgoSKfjNhkfKbHPNzuFWKfrgSd/lSi0qzf3dy7hbrH8OwQ8PvfTJ1z8aDPTzucuSVvwpjksCeHeXpErXSjsSU5It4w8WPd1YbbtNG+62fqmyk98ktjNJ1AIo4nZ6LKOQBV/BB7Ogegc3TutX6vYFXnxP4OSrbNWq9fAcB8LJcwpgsw7ZxMGv484WSHgKcCOwP/KiXb71bd/sCHfy91vgJ1bHK213prmycblJsRDj54vbFNnxat+7VWvnx57br5fU/1zdO49V0vFzcP5DmtbesS+w2nJy269ZOe5KBcqqz5Sk9eVDrxw/Y066t+mIxlXT92jnn9PtEdvzoANfFwcp/zJo1FZzulOj3mE8YkX2XyX079p8TvSb4ddf2gv73z9ez/duth8u9SvVd8y26uX5HHh/6r3S3ULfBp8k0KsvLwXhf0Sg6B87j8Wm8DTdUOH0cw4EyGEev3Kaak7HYOR9oddv5OmSfnh3XUYqGj0NX5XjqJLv9XjMJJMf8uh+cz0C3jf685cwuoPeg1vUc+nSGY8nXGbHK6uzL1oYNv44d5bFSwjI16b2SkHXKm7+SR+nOSn/1Op6nqOun8eh9VJ7/kWBRx9z714d467h7G/kfh3q1k+2Wtt++JZD6Wo61jx7twfxq/rj6vjZONpHvQrQGjh77+b9BVH8NrgjreZfl9slaMOQ4Yr7Ve/TMdH6tLn3Ry1nhCnDLvqT+J7Hd12HlSNwNBlG/S/cTPTidV3XznEwNU3bhcebysK3/6yJ7pVp11Qif9OcHZzqf19RTssE3DE833wPpdsGU3p5Nvf2vQKekBzu2Eibu2XGfyDa8Elqa670V3DTydTtRbOuHJSeOHg1X3ShGVck6P4PlopQfOxjEnhB8vqPqYVkqOeV0uRdmtqOo3d5Zp1Nc9TuCqm0rV7wepPKkvbNeTl4qeY5ACYR7v9Dzs6U6J+0uycvD1e2hyaG8FpsnB7f4BqmRZL9mjfGseW2klIKbi7JR7ra/aMTPvaW64vE+CJOWX5NbV2Y1nGvuu7q7sRCmIfTq3LPcH3YfeizMG1OSk1xrwLi+DRdSF9eLLl5e3/6JjHVePUFR7//zzz4/1wV1qHjf3OnR6MrzTezPY3yST6uta69VuMQ2vwlU+tmFD/tu3b694YzCN/HePG1Zb6WXmXX31ndap7+9O5J46Bld3+o3TtkvSYys2zM2r+XUbJvfdzmrNw//3//7fKzxKdU6OWzm+5KvS6vH+5+fn9fz8/Go9+XSU+/desqwcELs3ndi9J+1erefeDsO/kd5rJxgzTnCnrmuOe63X48il/wpb/v777zf2OutzwIkvY2bbPgVUOFef8jUqHx1j9zP9E1nVWXknWy79W6l9MH66fJ2ONL6Qb677krXxprOR2ZdJR3XBpy74nwIixpikvytfwgWmJfvG87e7Z3/Rhx58aCNRwppKn/wS2kYk8vL8/PwDW2r+89ptsu7pRK7theQDFW+0iTo/zmNSfl9dMz2Vdd/T+HltdPqmo86f4rfH7JQ+zYknD4TvrTUbWmmy1yRzcMQGeNpRYBvJMKnPzlC1snZf0i5BchCc3wthdzKJToqNZU9UAo9BNcmYJ7tYn5WgQYN1kE4N+aKPMBSn+XiPuqe0pBQ/yiC+WreV70fw9KAHfRTtcCZhTMKZpMsrv3eTfRrKWHIacDnpk+ubTsyyfLpO/S/s4HuaSJ1h5M0IY5CpMMj1mEfjy2T0doYU7++M5ir3O/VeN0+LOqe0qyt9pjLJaL01qPLAjwf9LprWyU5X+N5uPThPV9+pv8KAypcvX968z5Wb3lWGn87xnDDpVC5VnrjX+Q7db57Ksm/i4ES6Nj/lIH/58uUHdqUAgMmOfMKhk3FN9dZ38bDzJT8LTfYP85BSEKTLf7JBm8aD9XdpDAjRPkttc87xpGDXZ5PtplM7y/Yh86QNGcqjq2+H6b+afnvgKQVZpknbBXJSEMhGr4+ZpoFyYCddJ+ciKdYUXOqM8u6RhK4+phGEkjJNitMOyVqvn5UmYHQ7sJXPOxZ0AOxsnBisV06GTIv5FpqcuHtQx2+6npRRp5xIE+87R6Su01h8FuX1oAedUsKZDi+mMgln/CldRyOf+rl0L081Vb70LqiOV98zXllf7+RTlAwxB68YNOIGg3U+efA/yFp30Tnwy9fJJ99v0uHc1D/2I6WxvHerp/L3IjqfJ3Qyt6eyNsI749ZkeZ8a453z8aAH/U7q5mQXBKp7k02b1sYU1CmdR5xI93jKqB678+N2zEde63fpYreXnPLulNFar08C2684seVt+7IcfY8kU8vXWMgTOtWOXyBebfB0WBFPb13RdcxnvCz+qj4feugwzLLa0VX/JY3xLXUnHpP/4tNHO1+msys6Hjyv7EtNp2mTn77W64BoZxskPlI7qc3Jz1vr9aOiCeu7NojnqW+nduK96LcEniywZMzXfQvJhirrSMGUqpv/DsGFnhS5jw86b3I26n7VTSVspU6+2Gb3WEQiKmbWlfKwHfedsipKwbn6Zn84LiVD5kkft9nxfkLTIr4XXVXet7ZRlE4UUVF3CsT1dG1MyriTZwcIO8fkQa/pI53V/xJdcciZP63lyVGv8Zpwhmmux/qufteHu9FrvQ78e73S8ZnkkE7vdidhXW6ixKODan7EgnwXr9yZN7bwEcP6+Gh9ORGdQ3TltOzOcCVmemMn5f0oSo7XyTxO9/nbup+44sfj1sqnEJIT0PFne2mtt/8cNPH8oP/RQy73oc7ZSvKtdW+95XneBUJ83VHyV9Z6G/ivbz6yTX3MF4rzCQa+78mb6vR9LAdjXse7y+10rPNb9g5A2TfzdcJJym/3qg8Hn9huYU835ibOgcSPT9Ew6DcFE3ZkeSbdnHgm3t3Sz6JkH/k7nTwy791a64KASUZdW6fyTNhPHHRb9d3N4xMfK10bIye8Tzg69Zvzk2vIPJreg0O/PPDkTp04Baf1ugwFbSfCCr2Ud+X3P0FQodFQKyojOR0XrTYYuEqLOhn3U4CmyqTTVEUOxtmRoiI376yj0ghUnsh+NGJ6rG5Sfid0siCuLoxpLt6brIR9r/tM9ZB2gE9FNgEC86QyRVdOqf2X6aPn1Z9Op/LZ5TvBGa/zBOadU5KAusOFpFupgxnc8Kla6/bJAaBOT49bX9197upmP0ip37zmIyTVFvmiHMp56uorrDUuOvA2OX7J3ugcx93u5g5rrqz7aeMmPb5+4tylaxu3lPvOQC9e0mnoiRcazryejPauD/9F+hW2yX+FrOd3WJDydnrG+iPVwW9fd/5K6T079sQLbm44ndiTbH8HP9hXt+uAStr0OPHrut8sx/4nSsGd5KulsbFO5ftTKaM6sWuaNiXcJinZAWyzo1t1YMI/44r1eXfiq6uzKM2FSq8+cGyMN7xm27ugU1eHMe6WYFQKxtQY7vzRaVMl4XLHl3VEN7c6Gez6N9nHrrub7yf02048dY7AWjkCV9QpsU6RU2GV4qhvBm78Xff4aAAnOyfyFMSxcuELwl9eXj/S1jkpSXZWylaeCTA6h6SbmDy9VfW4bNoBZR87Jyn1g/We0g7ErtDJorsnWSFZWVApM39S0lfaTGvGfHROlsvulO6DHnRvOl2XpzhjwO3Kp++iybg3z9ygcDDfToGDUck5SrJhfmLMJMedE+TfCRcpN254OKBWfad8fG1MKUobHDRMJyOykwHnAfvI/A4+fYTe64JOPsmdDPCEpxM+di/97pwAj4nb5ViYJkO7I8v6gTP/o4cc7kOTvk95OO+TPkz44vpNTOP6qtNJ/nfThGnED9qIDj7x1BN1LHWm63I/p42P4onlkx7tMHQnr8nR7fDbuiz5WdzkMN4wIOMTY1f8g3QKmDZ0wk5urHQy6WRR1PFone1+Ms35iUepbfqdHW/Jl+l0fMLDHXV17z4TpTz0f+wLdXWe4h/r8JqZ9JRxe2qjxqvzw7v2Tnif6Le946lzCur3WmfHvjrHYK23b9xnW26D+f291utTSOkUlNumYvMuQReUcp+KJ9fhvhkQii+mOQKdFDWBy/yyneQY+JN2v5OTQl6uKPKTBX26MBIvru8KbzuyA8Y0XndK8Yqy7MgBLX4nnqjIU38etKd7zqF/K53KaJcv6bmEM51hnJyIZADUtY33SncAaq31yng1Frieia+OR3/vdlAtM18nIt82Tp3HxL5///7zH/KKLK/iJ334xxadcW+HcTKyJufGednWCZ2MgY17ftc1/1Uoyd597XDRu8/U8ZO+79rtiO0kPnwv/X7Qgz6Kkv09+RpF09qw7djldZDGJ1xrLSY7ftqk4MvF/S5BnopifuvAtP7tV5jsS3X3J/l1VPlTUMs+HftQvPh3UXr8jvqNmMx+ezOg82/827ymDSv341ZiO6zTOLMLPvFdWN2pI7eZ0iYfh4HSjoyFbiPNWeMZfa/ki3W8X0m/B36lfkzjeEK7fNO8vSf99peLJ+ocAt8jdcECKkKeXnKgpgvMpAVjxdwdKyQomP/iKSlw97tT9knRVjk/1uCddStnOinpr1arHZ9k8sSnUmB95styeu9CvQeg7ZyTjyIrzO63nQEbJ7e0m/goMgiwjcdJpwf9m4k6wPqBaZWe1iUN3nSikDuoyRAzvhQZb1KgoduwOOm3+5dwrT4+uZUMoqennzvIxJsq9/z8/MOoZ78oF2ISDVS2aYOZ90y74JP7fg+M2lHxXe25T5Un4W3Xp07Hc46kDZA0n4vImzeYOko70LzHkxmeeylw9aAHvZesN6f1k9YRryeHLdVh38RYY/yh3V7rgX/fXnV++/bt1clRvzOP/eTjytVO+iRZ2X9yYCzRe2TY6eQib4D4NG3SkTsbtuRTQZf6rg+xr8ObGudOJsRBBxjvEXiayEEk4035bQ72dPKc+Ow2knYbHGzjJNDiOZV+229KeRIeGYuMmxOlOTHx7nTOec/zU396kluyIT+Sfmvg6dTBT4rGdSSBTQNJ5ZmUZQIDlueHdRlcuvIOPpH3dO023A4VQ9W71tt/qrPCZP08TprkbQeLQSUvBrbtY6smK6/3BH3es2De2/ZpG7v73Xxj+Vv6OdU9rRs7Cb5/S9DrQQ/6VXRlXXd5J5xZK+t6GjHGG+84p3Xo9kmTgb8rO/Hs3w42pw0HYwrrJ47ZSSoi5kxYX/fpUBSusS07AFOdO8O5k9FU5lZKASVipoNQqczEl+dZMpw7x3Pi10b1FQz9aAP3QQ/a0RTw6NYIv0/qvFqGNrYDTpXX/xLKgNT3799f3ffH+tLr3YGlqW/M0/kxKa+vbUd27aSTRixrjDJ/rn/SWYXP3PhwIGCHfx3unDr5OwxKtLMPHMQx3vC7y0/eEu18ijT/fM/8Tmldec5pb4B0/LrcDi93tPNxnbaz2bqg0ykvU/20nz4Kn3/Ly8Wv0k4ANjiZ/vz8/CN6y91ln4CqfzOoQAqPqK611tevX9dff/21/vnnn7XWz0lcR/1r59YR4VJKlc8ODHl1YCflSwqWgR3ucDAKX23QUOcRXLbh58opY7fPI70MpnHsCJzkyUGy4jv981Ea65Q+5ZnIgLRzVj3OqR7yY1B3Oj8lAypJR9rruwwM/uan6nl+fj5SvE6vOupUQirnne8k22S0dTK2fHzd1f/RQUO39XCa7k/UOzsn9or8mfeKo56M1zT+Ly8vP9aagwO15upfiurDf9Ch7ivd2J1wTYa1ebYzUuVS340P1jmktKHBtcedWj8SxqAbd4l58oZ9K6yiE/X333+vL1++vMIdb/r89ddf6//+7/9+5PduPLGRtHOo2AfW4V1R1mfZuj3WWzwW39zU8Wku19+10WFM6qPvl12UMGaqr4jzl7YW202nraq9RJT3qdHf0Q5PpjwfQZ0NMtk6U9qDzmjCGtrkac69vLy80XNdoCCtL39YhoH6td6eXqU+r3VRGPLlS+/aVVv1Eu3uhd2pD8QU+wj2N5JMTu3ltfIfEXnj0yd2Kk9dE08Lo0uu//zzz4+0ykvdS/wo+fpEEP2+FOg5OQ1FmTpgZJ3Lejt8ThhR/PIUE/vBe1++fPlxj+XTOO6wJF13aTu/yLIoe4sf+0XTfElEvqqedJ9z0+9KTB/Kzfoj6W7ymPxxz3nX6Tq6uiccpY3U4cutGPwpH7UzceBSuu9PgqDxmep3nVQ4iRdPrjIceS8ZEVY6aSc4KX0b+alvqXwygF2P2+Nu/BRoocOUlCvzdbvVlr/bSPylft5qgKXxmYjjnOq5wosXf+cITErb6VTCk+HaKY5bjNvd+HykcTwZMPes/xad86DrNDkDa82Y8F6yjq7rdL9+d/y43s7A3PFuw5pGQcKVxKex76qsWF/Cms4onX4n+U6Bh7Xyydmki41DnfHXlWHaTlYJ49fq5+lUjz+7dLaTxj9RMvJTQCddJydgRxMGnZSb8t/S/o5Oxvwj6ARnbl2/D3pLaU1NlNZ4ylN1O20q0+nttd6+f7bWH+1qBjj4AvHSpwziU1dwPSe/JPU3zU3zwD6k/iddmb476vhyWfaTv+0/TUEJ1vv9+/dXgT37Nq5rwhry9PT0emODaZTJqW7q5vOEKdzsqLanNZKwPF3bx7TfcWV9uJ4dJb/In7p3lbo+eH34XqVNOmKah5NdPJV3+zv6KF/npsDTFUPqvZQMty6tM3R9z4Z7UuadEuZCopKg88G/3WQb3mW9RWmniUBKDo3vWbFRLp2RU5ScjVJQjPyXPFK0v77rXtq1SDQZryfXE6X5NN1nnt16sNLs2pkM/JQ+KVTX52+fpjIfE8+Tgt61/aAH7ch6Yqf/O/04rbluPe/0X1emWz+8T/1LHcl7p+vkav4TDJ36yO+qj6efUsDjFiyyIZ70UKXztHG167TC43Taa3oUwTzs8p04BCcyT06BTzulE08nhrzxxJjRGeXdrvKuzQ5XXNYYNvUh9cd5O+x6YNCDSMnxrvQiPvWw1uuTJqf44OuEF8nWdyDD9ht9DWMM1y2fiGAfeAK1syk75zetJfpJnQx21K3pUx6KfFig0qhT6xRn4oGBpeKJ+vf5+fmHj1cySo897vyazi/qgk81VgxS3UoJYxLe7PrQzV/PV8rS6UWTH3NyP2GXfZxUnnPEdZpOZW5f/YquSHWl613e0zInZP46nD2hy4GnSQD3DEhRSac0T/ZJGVpAVtJVR9VTypygY0dhrdfGP9NthFcdXWCnPjTkUx+cRpkw/cTgNw9F/HcL8tP984N33qlwq/4UNSefJZfuSGoC+BMD9Srgke90/9Z7U5ueO063wnbAKBkOnQORyhsEnOdEZrfef9CDTMkJ6HCmMxqvBAJSnVOQObWV0tO6SxsXJ+u260cK/Lj/llsyyCeyccc2rMdd5lYssqPnevmIYtKh7JcfVym+irfOKExzKF2fBKbIUzdnahfdGJmM/25tkKe0w1ztsM36nuZgknP6MJ/77dNR9Zt5ksy6INUpOf8Dkx5UlGzSZHvWd81H+wSkU50w4YbT7OgWj9VW+Ste+9RxDjbxBBT75Q3wrn/GS+oknpRKfax81oWTHmc5b9C7/mrbGz0kypR9qXb4zsH0fifymP48yZjs8fNYOr3rq+Xu4NNuM8X1k1fijQNPU/3TxnVnP5Dnqaw3PZLP4zp8f3fNdlN/SInXE0rB2FPbgdTNlatl3oODaUyv0nHgqTP+0717E5VOunfiYFD52EBj5H+tn4GWL1++vFLKBh7WTSXjXe0uwNXJNAWg7GyxTeep+3480ACUlLeBy0DCABSDRozQJ0WZwN2g4EWdDAHen9KuKIXiz7JM824CvFQPycp29zkNIHVK2vf4no4JGMhrJ8fdejvJ96AHmagb+LvLS/2fDFkT83freoc3O/K6Mo+dQUWMSetz0ispQMN+UZ6pz7v+pL5TTsQs4kvxV/eS/ue9ym+sMXXBJ7bn/lad6dGFlN9pKW+aN+RrwoM0Z6ve5MikE0+JF+Nv0vFpbiWDPG1CTJg0YRbzsS7LLo1janeqI/U33XvQf5eS4905aTVva+2dnHqYcCSlVxtew9STts/rPssm36PsP78Tz3jD/lqvm9fEP4M+V/3Czh49wfWuHmOBN7etjypf5+B3djOp5M33+pomnV9ytOyTbc77VyhhcQpAGYeMdZzjScem8Ux+EO8lPyZdT8Gorgyv2Wb5RldxYbd5MgWKTtraxV1SHUzryl/tZ8LSW+RV9O5H7T4i6OTJnBZ/SuvyJGXBQAl3/oq48Hn6qYJSFQWngrHip+JIRnhHUwCqk9VUvvrA/Db4abQmhWdDNuUxn/7XDTpAxUPa8U6gdTrmt9KkOGzQe27tHNiTtjvjf0rrFLTzp8h+16adDjp/aSeAc6ED5IeR/6BTorHTBZ6T8ZMM09OAwMlaTuUqLe2mJp3KDQ4/DlGBFK9pOxBJD1a5Tp513zvBJ7I6kSENefPMsSSeVh3U+XWP6d1J27V+Bp/IR9WR5MFgl+dWJ1eXv9VgI9+WecrrduvbhuwJTlb6bsPBOFI0YYvLGZeMQ8Yi8ud6md4ZuVNdJ3kf9N+l5HxPgae1Xtus1POkSadOwa2ufPFqvfX8/PzmXUO2P4kdxXPZdX/99dcr3yU9vVF04otQJn4SwpRs6ZQntUu5dydwKDeOkX0yfgpvPEY7Zz/ZKcn/O3mSpdKdh/wQX0tOt1Dim2nTmjgZu/ru+Ot8EP+RBV8inl4onoJK6c+ZEsYln2atvOmReJ/6mmzMyrezJTq5Jlt40j87G/cKdT7dLfPv5hNPTrvFKLtKJ0JLQreQqGSLGAUlJQeiAlEMPq2V33/EYBR5d+BncrImp6NzCDw2/itrOgpup67rt0Gv+GbZtV7/g0R98/0alEPigfJ0nmnc77UgPIc99ubBgbOJh6TIOuM6GfLMZ8VK5dv900NSwDb0+Zu8d3LsnN3O6H8Y/A/aUTL6kk40pXV6y3zzOu8wx+0Y5JPRbAeg8qZAFNfn09PTqwD+6Q4n6y4+iUtXj+VXHUkWxJ0JDxMWkfiPpvXN3cj6FFbUewWTEetNkfrtf02lcd2NN+dWYZ+xwjJJcrIsKbuuXKo/pZ3M+87YJ64436mR7+/OCTCmGUeSM5D4fuDJg95DycH2o0VrvfYZ1nr7NMEt83DnsBtDqHuq7PRERpUrXr1+q/z3799/vK+IebughIMp1IvU2ZZZp6tOHOnqR7VLeSRfofInv6s7kcU+1IYG7V++z4kYZF3k35RZ4VCNo/2E5DcUTwk7zP97KcmxG6eJJl3Neen7xIn6F21iCPGlPvy37uQ3MY11MI28eSxNyS9Lcqzyu3VOeXX1TGkn9lji61Y64fuEbv5Xu9She1MauM7InxahDTxOnFK0VOLsl/8BgsEZK5vkWKQTUlaQyQHw5E3BstRfG7WV1jkE5tWTthYq/+2AedKLWylXB8zqm45AKWTvblvZT5SU9nsMAteXlH9q++paSIDVKewpkJSM+VRXF1iysd+V2fVjl/agB02Ugk6T0ZoMgJN5R500GQgpb5eH10kf1bWDTnQGSp/ypbE2BDscSFRt+CTRWvlx7K6OHVkH2rBnGwmLrGNTfj5qVn2hw+Qd/MKPVK7KOqDE/p70e5oXO7yYjNL3YFjVvZuDDOjxN+djMtzr+vn5+YfhT2cg/U54xbXQGfHvkcEDkx40kTEm4Q11rb93+sFzrQuy7Na6fQba4Xy/U+XzKXU+lsx2n5+fXwVbqn3+Q2gKxE2+BPux8xE7p3jSqVO7bJ9jSV+EmEf957GvDY2qj/mIJQzq1e+///77DUZ3cyrxPtkSCZsSdp6Q6+4ojR3nLIOViXfiiXGAvkV3mqmwhsEmYxHzsU4Hk8gD2+2wsiP251ZKft5Ep75lh6WnemtHp/xOdHPgqeijgk6mzjBOiorGZJpQrMcL4mRBc1I7WMPAVFdHMtILPMi/++IgVceX++h+T8Z9pXVHLAlKVOpOc3sMZE0ODpV/B8hT4Oe9C+JkcU5O2nvatTHeKaVkCNmA4ZhYwaf8iZdEVrgTSJrfBz3oCtnYXmvW9yy3M2BTXVfXfNKj/G1MSQYQDSwbsWXM2fnZraedviRNhnzS7al+t9vhZgreTxhG3PCjFqneTr8xKMX7xYMfQzevHZ0a+6d4MY1tp1cTzpO/qQ3OM+8A02CngV/fXWApYcupcW3e2Kf3GLsP/HlQIgdyiDfpZCrz3mIDTrZlpxc7Z546rNYrdWTZ2YUhFWQqB73ylF6s/N789ckmy874MfUt3Z9kmPyLIvd3whbyTz9nrZ+PdiecMe9sx7a2ZUTsqW9vfOzwdKLJv7xK99arSefvAk9O5++EU76/82+6j/sybYB0/e7k0MUOdjx0dfF7yp/Su5jIe+g99RwHnriouwDBZJgX3cNh3w1QUjQvLy9vDFDXSaVLpZ2CJd4J6BSbAazy2qlKOxcJ4NLC6JQl22B57zJPTl2SE8GGwOzF6x0G3rPyqLGhbCgHnpA6mWdTeqLJWU1Gb/Hm8ZieObfC8bFP7wxUWd6vnanpsQbvXFvJM9/Xr19f7SSkcl4b5MtKP/XzHoruBKQ/SqEmnbUzsB50OxljrGepN21gFDFYQ4PPtBsz6oTES5e3flOfkwqHSofUrjQxgM4A2+a7jTo8pW6nM0V+eCKIeSlP6vjTNU0c7B4RN0ayXe9E851NLy//O6lUuqp4dN10KBJRrpXPGFS8uG9dfZyPU4Ceddk4Z/CxTr65bs5H2heee9TPlg/563aFrf+JN9x1/vr164/TT8/Pz+vr16/r69ev6//+7/9isMo71hz3SrPDWePS/QV6Mqg7Z5l9THRVj5/k3zm0lfbAkF9LU9CJefzeOAZp/MjVpCPWmnXCKZ+2yUrP8qTsWm+fvCAf9ZQHdUXVX9jw9evXH33sfAXret6jTkvYmfQOv1MwqIi+RVe/6/U96hviY9VZ79Cin0KZEWe79xHS32Q9xm9jQeLf8jI2uK+ue5qfzlN9Srx25dd6+4g9cdaBo+rr5NPQR7EPk/yQrv+dn0Ki3Wj88Nic2EGUgYnrZ6315mQieTI/HiuPG+slL4k3f7P/uzHv1vGO3n3iiY2TSgCdkXC1vqrzpL2TBeJBKioFtquPSojX5M+DyD6kPrutk74UL2ut1sh3fVR85MlgYb5ToIWKz4Zw8eQAEu+nNtg2J3+nsNmHW8nz1XPgdD57B6jbubUCJuBVvhSY8jcDTkkBd+0khZXk6J2Ee8iadKofdmv/VpqU8y6/eTgF5gf1ZAM7rfVOl14Zy24t78jjnAxx5+e18YLvxVvr9R8yMCBFWVjHdH1Kej1tutwig5M8aTw6fLMxbZ3Lvhl7u3YTXpGISUlG3RzpsOue5P4nXZ361xmjNsqNAcYM4kh6h5Nf+Do9JpFwKRnQiexUsZ/O05VnmbReu7L3whWnTbbEg34d7XAm5aeNPc2fE2x6r+4wlhRfxBCST0FxjdJOt82e5qpt5cSbdXFH1L1OO1mnnWzSdWqbQSuvzYQxrPfp6ekVXpsY7Ht5+fkkCfs1neYln52tPI3NCaV5xPGzvzjVk+wdttH5Ix1edO912m28O0iVMKTz65OfM9kRrrOTQZe/8nVjWd/duE59m/Ra8uumdbOzr6/Mu7sEnj6akhG7C9AkhdUJNi0uBl+8SNZ6G1Bx3VzIVjQ2mlwm3euMJpIdkmS0u79sJ8kj8eudc07yao9lGSwqRczATO0YWSnbIKChfQ8HyjTNow5ku3QHkSbjNhniKZK/U84pjYqX4Ng5JL4+oSsK58RI+930Uc7kg/aU1jy/fyftcKZ+d/iUDDHqSTr6PH1T67mI6fWbbRXdIrPdnO90Q9eWMa3LU2TMJM64/cQrsYYviS18MW4UVk2bNh3/9woeGEPcx2SodwbpFazZYUzNOQeX0sml9I4nBk8Tf8zDfu/k9KC39AhkXafJKWNQoOan7fi07jr7aRdI6ez0DnNcp30Trt30eoy6V0GqehTPcuG7adPJC/JYesk8J7/mBEe7tq6Sy9C/YFoaN/bJdXRPvaQ2qz36mdbd0zhP8yP93snKfgDtEabt/Gzrbq8B5jPWMD09pTFhTgo+dZsfEyYmebMfp37clbon6tb8VId1Syrbza3J70tzk3qxW1cn9FsCT1cGbwq2TAt2GjgKuctXQq/FyCPvVPBVlx/HsyIuYtk0sAzEWAYnfaw6uqBXl8953E8b/+5H5wglx5H1lGzJL5+LvsUJ3Tk6HSVl3YEO+WRfTF7Q3a4vFZwfl6MR0QWY0g6BHQoq0xTZd76ik2j/iVFgxd7d+120A/YHfTwlPZGMxF9JV3Gm7iUAt1FHx/vp6efjX+UMkCqY8uXLl1eGa9K7iZ8r/F+lEzw6KddhF4/8O/Ay1Vf/2JR0bpX3mLAOyvcjKPWfPHFzh0Yf+8Br9iU5AXY0jAfdxoWDS3YAuh3pzsGxTLs1nfh/0P/ogVf3Ia6/dKLewSd/EnX2zTRGJ/q4W+elJ7yu6v1NRbQnedKJNj7zVB+Zb4clnYM+ySvl/QgnP/FO38sHCuq+N8qNUeT55eUlboxTlg4+rZVf5M6y0xxKwand3ExzyHhDO8V+qevcfdImx1qv/Z10kjZhTApMJd+IbXu8SfaPWObqPNxhcEe2Pa7i3ql/7DqTXcTrZBO5nsTzRJ/+xJM71QWi0mJL+ScDlIrXi47gk573Zl2pvUrzv9vt+um6LBtSMuA7orKdlF2qyzKzIcxFm06Gud0E/MyTHFAbXKT3OAonzpt54DwhP5MzYCWZ8jk45KBVF0xiWjqq2gW/ODbT/XsZt0kh/w7DeQL1eznmD7pGKQD1GWjCGa+PpJ+NJWvlYION4Cpfddp4Te/EKR46HGT7fg8FdYHTdvKZxqszmOu62uSOmp0qvgzcO5LT+ygoC7bJwJQfpenwJ/XlKpmXhJl2DIuYbn46HEljaZsl4Uo61VQOQQpAJTzqAlFFDkZ9ZLDvTybbFt3vB+3Ja9tpTPf6s37odEGyKzoH80oe48XT09tH7CqN/9hdH+rQKlebHtxkr393K91Y1G2cT37MbizYz9TXKzTlrzr9iJt1qvtS9/heKPJK3UceiDt+PYufOtkFD05kMtmw7BNx1XzZ7uABAcqM/bNOTz5LwiRvcvA9tsYafrrAU12Tt0TTHLFd4bSO3P9Teq/+nnDgakAq8Zbmp+2IK/X/8sDT6WBMQRnfd/3TPS8QG91W1NXWpODXWm+iwTYWO2VG3tivnZxOnGO3naj6k8DFSqp79C7tmEx9t0Hvsslx6Nq3DG5ZXCyTjHkbGNz9MMha6Rt8UmS96poUdEqnsmZaASD/6jrVk3ghP13gi7K6ha4aER9Jyam/xdB50L+XaOilAI51DteM62AApYg4w/dF2Pjz+yIKu5KhyDana/NsXdjJw/1LBvGVAFQno4QdSadWXm+kuM+F9Xyx+svLy6vgE50SOgYJl7q+7MjzJzk5aezrt4Nz5sV2TsIZl/NmiINI3WN1yQkwLrn+yVnp1s+DflJyLh8y+j1kvWA9mNI6PXKil33/5eV18IgBjcqT7FrbgrS//QTH9+8/X7Td9bvy0SmdcNJ9nHTZrZTGIN0r3vl0izc/vAlgmVadf/3114+Ttmv9xBrjNuVD6nAmUecvTvqg8yeqHLGG4+fga+fbMM3Xa739N1X7LDWf68OXi/PaeMN+dNjC6+TzTPlOZZ9kc4U6HbDL5/TdHOr0UddX+0qdTXxCn/bE064TdhJ3xj+F0xleDLKYymilgi8ngQplrdd/ncn6PNCVzqBVfSejMvWv6wP5ZjuJ7Aw50EJnIO202lHYTfxS6p6wTPNRVAd9KEfL9WrwyWW7gF3xyeBYZ3TUtcfVSjoZ41TGVSY5BZ2T4Dp87cfwEnAUsT7ycqsxsFPSv5NOneYHfTzdCmgfwcdJnmTUprW01lt9W4ZurU0b/lVP964O609iCMvT6L3S32S8Tfg5pTF9ki1llLDIj7+QjJ2FVw7sUG6FLfynwU7XJ4y9hShL2xDuv9uhc5fkbizpHI2EOcaV9PE/2aXdaG+O0FlIfHUy+iz48NkoBZ8edE7Wawn/vV6YxvXHEyNTgCU5q53zl5zBtBYYMGHAfMJNrkk/lVAfPqZsfUsfoNPxXtdJ3l2fuuurNAXyUr32/azzjUO7uZGwxq8VSfjmNt2fhKFp/kwy9ZizjtRmasP9Lhl2mJP8k+kRugo0Fd5wo8MnnxLmJJ+JG/E8pcs+1ny/Ze7Z15vwjdStI36nPDt7ZFemsyVSm2kd3OIzfdrA01pnzscVg5f3uwXZ1cWBqfw+osp3UBTxdIwN4hSgqDQHi7pJQeqUaQI7949Bl3RCioYqJ58VFWVExWyDvRQwnYLir06cVX4rZ6aZn1vI8vEY7EC8ykwAmpRRUs6lDCfl6ZNM3XPRKciUdgQMGk77CMPfdf1Op8LK83fy8l+nexmc96Zbcca/va645p+eXj/mYD1EQ8g6erdRkXjrDAYb01fqTPWtlYM/ibwWiZvGlQ5nuBn08vLyyrGq+uhQMfBEh2CttxsgaWPlvQ5/5wCxL2yHtoFPN1R9ycZJut7GPzEm/fbjdTb+06N3tYvteU8eHnROD7z6OLL9u7OZjAG2c1kvqQsKnOgS+yHkm1hR9XGTnPYdNzzKXmS/0h8x2Oa2nknzMckq+TJJHpbb9PSGeU3Xpslf4gljYofxmRs8fAS+ZG6sqU+lsx4/Pm95k8dJ7l05+7P+EHfYJ45f8sl3/k0KANW8Y0DJgafn5+f19evX9f/+3//7gSVfv359s/Fh/EnYZfxywDDNAcqhmy+JOv9qos6emGy1NO4nOsQBpJM6d0GpKzj0qQNPa13b7Z6Ud7dIO5CwoqnF42CJ6ze//E0lRF7MX1rQk4FhIOqUTeLb/Fc+88n73F3xBLbR3jkO7j/veae7iPVcWVw7SjLezSHnT+1OADwt0rTz7F3iFHRysIpzsgty0bByP7v796KHwfwgkw2jRO919N9LJzoigTfz8D51ZBlbdVx/rfXq6H59U0faKNrJJ2HqhA1V5orRNdXTGSsOJFWaMcQbGd6xr3Jus3OKTGmDqHDNDtR7aIfzyUkoPV4YvKuH4+a+2hmwg1ABIzsDKVjFHWk7GOYhYaCx6kHn9Ag+3UadfcvfyTaa9G2nZ075uFKmC17V+rM+5bosPUo73rb3Wm9fIWJdNAWCqCtTQM/XTOt0wK5NUtLRV/yqCtqttX6chLWP02GKnwipdOu9hLknrxRx2tX5lnxSy595uo0tY8/koxpfko/jE1DeAHEgKfk/uw1549SJvD4zvVf/T/ZYos6euEKXAk9mcDIifwXtnP5dMGat1//yUAqljDruElT0n4Z/KaY6oVOOAl8gzud7uyg083HxJqObfXOffe0xSsDSBXF4bJd1k7+qizvGzMNoMYGsynKM/JidebMs0n1+pr4lmhYvlRNlwvSurc7gdpCIv9Oz1ClvOpraKXby67FhXXQ0GLza0QQ6VxTjexXorrzHyIHPe/P0oGtkY9XrutI7w3UCzpTvvQEEBweSjq5rBwNsiPPdChV8Wuv1+4jWeq2bGbCiA7HWepOWcMdytazSekn639QFh+ra/DhgZNmRN+8U87rqLgwvOXM3mcGQwnC/H8UnjWkb8JQRnRPKZ9IryekxbieDmE5PjW3xbpvA+ttz3/hTY2aDnLvR3c5xyZnYYVyxE2FZpDVNrLtVB3dlO33f2Vjk9Z58fEQ9D7w6owrAlD3PpxPWehtk6uYk86+VMYXBhAl/qDftw/B+1cXy7EOtPer2Dm+mJxxSnoTRLudHD71hbxvd89o2cOLNfT6x+zvfgfcSplP3egO89G/CqErn6VT6j/Wb2MRgUOUx/yeU8NV4sNZaX79+ffX+Lo4t+0s/zWPONUEMID4wz+QHdSdrv337tv7v//7vzWlbnoJifW7L84D50jzxGnWwkcHd1Kerethl0lj5FN4Oo/x7FzeYZGbynLpCx4EnG0YTQH8UTYrdStoG4JTXyr6ry0EgBp2mOiwv02SAUgF0A7zrM8uxPo+heUjtGgjpFCRj26CX+uDyBq+6l/pnp9TOTqJpTnQ8mtcEdql+Gy82amzMJCVOZZ6CTz66OgWR7Izw95VAE/uXfieFd4uCch07Xiz707IP+hx0Mkc8xwyU751npJN5k+Zdwg/yaj69JgtPajODetTOB3VF2iyYDHHz0aVbN5OSU5MwKK3NjrfJxkhyq7qoX5P+7wJDld8vfeXH7SYjfEe7uUld7PT0mB9PPvl09oQlzDNtWvCRh27HOb07I+EMqXMq70mnhnMqc2W9nOqIHd2rngedEW12n84vsiOWsGbCHQeQJjvovfOKddM3sS9CfVB+TMIa+x4sY79mki3zJoz63ZSwphtTB9oqD/UsdXKVeXl5++hid7K2xsT63u2m31f0g4OCkw/jdjy2DvRU/cagyecpDEknnhLOOG+HaYnHq/iTZDMFRyvfrfo66Zf6PemFZKt0+fh9hSYf67S/x4GnroFpkd6LOsO++8203Tfzd4NKo559TUGmtd7u9lZ+fhc5yMM8LO8J3h3lTDQFs3yfediuwSI5JR0PDk51gaKi9F6rKb939e1UJD4nSk4G09kf8tDJg3mTArYy7hyCTkm7nD9Vzsr4Xsa/53aaAw+D+UEn1Ony9Hsy/n/XfEv4koxZ44iDSrU2/S93NBZZl9vr6KrRb5y7SjtDKWHdrh5iEuWR8iSyQT9tUPDUAE9E+CRz6sNuLnZjVs4L+2DnwIYwee0wZocPXRrfveH3cKQAVcKuJIcHJjzodxLt9bXmjVs71SnN6Wyj0wUT3nW276lNy/Vf5ajPiow7DJoU/tjP2a3d5C/Ydp42038nJd+wyEGhzi/hidvkC5YcHfDxSeYT/6frA3me+pT46K7JC8sTL9Z6e3o2Pcrtj7ElYUl6xC5tgrAtnsDt5m6HUSe0i0VcrSvpilO+pnzvCTbtqNOdHV0KPNlI6hz0e5KN99TWqdGavlmXjfhyBshDTWjvLjq/lX4KMLFtlrccU/92gSr3P9VVBnQ3bs7L9roAHa9TMIaPiZQx7wXhIFXVZfCtawKZHYF7LjLPH8tnKpeMk13AyNH9TvGmvOl+tZkew6ARdrKOOyfiMxoSD/pzaDd/Et4kQ7+7/1HU6eCEmykw4GC2gwlc4+Uo2GFye1dOfO76dsXwmWSwVo+B5jEZz+TfDgx1MmXkvwFn+50jZaOfu/71WF4RMZ88XNGJiQ8+ruJ8zJ8cklOMISak3WafdCoHIeXn4xV0JIw1D3rQZyA6215HydfgmkqO7A6PSPahkn57D9U6NI4UltQrQ0oOddqp2v/rr5/vFST+1HXnaKaAhXV5pU0286+g5MfudLex2q8F4ZzoNutLbvUYm09HUbZ8BLzKkOfTIFQaA/JUfHh8KZPizz5bWhc736PDj8KZemyOj9Klcl1wit/sZ5pzSS4c310ZBw5vndcpnuLPySnrSfdMAcWJlyt8n9Cldzx1hmD9/giy4b4LrkyKvAvo2JCjQeeF1jkJzE/qXtxW92wEO/JsXklu68pudlKwVmY2vq1si1LfuYviYJAXtfM68JTqSo92OABlWe4CUlcNgARclm1yhHnPDuha640CdWQ/KXSn0QFwetF0r6OrSvXhaDzoClkfJUe+y3+S/iuoC77wmuuduoybD/W4BNfpWuuVM+Cg0+ToEM/WOsPKpMMcDCm+3RdjCes3rna2Rbo/4ZL1PR2oKpvwpHOEeMqJdTv41GHL6fxznzw/fE25dCeXd3gwYUy30XESfEqbGo/g0xntbI6PsrP/i0SnNNmp/K58aS5TR57M8U6XTf5UCoi5js6/+fbt248/puA9BgOIJ/W7/JMqX/fp09yi307t64+kZLsnGXY2R41FYcBubEz0N51e+SsY1D1N0fVpGpfkqyR/t8i4t3v3FtdIhzXckOgCUU4r/PK/0lU+tsk1urOJim/Lj7Jxn12u8vp6ilck6uyGrg81V9K4T+sy6ZZkb53yfgue3/yvdleFei/atev7nUHbkScTDT0r+LQAqcC5aNOAUqlwsZeiPyUa84mmgJSDRSkYV23YaUj5Kq+dASpT5kknruzElBx3J65Yr5WFHYMJDNJ1J5dKS3XwHpW7DZhSntXXFFSiEp1+O4jEfJOBRGMqjUWn/Ka+PhyNB91C3Rrr1vzOsLhiIN+LknHNNOvq0jF+x5CNuLQzWbpx10cal+RtwkzzWfxc7XsngyuPjFee7n7S95ajAzfTDqJlzAAVf5fu7urq5HuKQWne7Ha+K++0SdE9pj3tRvu+3ylIHErj88CEB30WqjnI00D+MF+a22muT3i0s6FOeV7r7cbBFPhg8KjKEjeqH5VeQSqfsE22PPkixiRnfNe3X+1TnjrozkdekzxYR+pTetKD+e17WK7OZ952NlHHl/vqcUz8Vj7qd+JEh0PGEONJwpn6p9Wu7s6PKV7TO9wos7TBM/lFrt/r8d44N41b0ZU2U3ziPbyd0k3veKpGOgPpXnRiGHdKNwUQUr2JOgfdk83/bmcFlHYGbPTaWKZCeXl5icGWSUGn9GqjC2ZReRRfHl+3mwJWBrWdoVxy656XTvycPgOdFEMdbe3Ic+ZEMXeydF6vDytgGu7J0J8cBX6q7imveXHwabeWi+/kRFw1uh70INKJgXdaz6+cb5MDkLCEPNKY5D/k2GH4/v37qxfAlj5jfgZzGGhJei89LpacLdIu6OS20hjYkPYj486Xfu/GNuFtpacNlq59BpW8SWTcMtbteGP7ndPIPjAfA5EuT9l6U4L/BESc8aMNzpf+Yag79WTMsv3D6wf9pBOn4lc75v9mSnYLAzLJ50i+wb3m8YRztsUrbfKLXM44QZ8mtVknRfmYFwNVtFeJOVVfYdfLy8urDeZpDne+2kdQhy0JD1Jauu/67UtVet3zXHJwhO/Yss+THnlL/esCIomnwpTKlwI1XV/pfxSW1HcXREpY43vpsbqES8Q7+1Qep9Sv967jU9tkKp/s1t16ObF17R/73q3rbrKfJvotLxe/tVPdhE+GvsslAXUCr4GcDGSW5SMRzJscgqQMUlS5y88+mlJ/Kr0rY8PXfZ3kTCPYStHkRd3tDvuZaY8dX+rqfF5YyVhP6R3duqhSPVYQBG0HhahIfSw17TxXPjoXdgCq3pID27XiOt3BO8lzohQf9CBTp6vWOt89ner6CDrBIn47UME1WoZ7dyKo1jqDS9xYMG55t7ratW60A3FLn6e+uy9uyxsU7rfLdu05YERML7nWI4sOfBG32V7JmPJ/enr6UY8DUCQ/vlBp3dwg7hkTeb2Tv3ElGe38i2r/Jg7RCfj69eur+3Ywqu1qt3OKKJ9pt/pBD/oISvPSwXrbbC57Mmc7P8l+SmebpjK819nmXV1lA1aesh+9OU39UdcMSBjLHGDiialK2/kKltlHBZ+Sb2Uf6mQToQ4dOHhDWVSf+V4tbmYkvc82qjz9poSbHY/JNyTGcXw6v9u+ouvyJodPMNW7mrx58fXr1zeBqSlPXTMt4Q8xKPFH+VwJOk3r3brCft9JvbRVrpD9y6KPWj9V9y2YfdOjdt3E/Ai6Un9nmHLh7YzWzliuezTweU2FsxuMDnjMS7dT7T5asXT9J3+7/k+BtsSHA2XOw3orfxnsrofKjbstBWRMT4DNskle7HPHK69386aTiZWOlR4VtAG+AI1BJ+d1YMn36QDUSQkrYLbn/vj66o7Aw4l40FVKAYpuHl0NQP1q6vSG9TR/0xjkoxF1nzppcuZdZ8KRhEO3GGCpnzvc7rAqle/qmurnhg/z0mjvjOi1fjoK5TiV0e9TYpVOnqa+U74cl2mee8OE15OTWvzXXPEpJeOKd5Vp0KeND+IOA1De8U4Oz4Me9Fko2fkpULT7PtF3O9rZ5rt2JvIGN0/P1jvrrGNoH3aObWcv2+a3jW+Z7II8H0UT1iT/jOXWet0f40F6GqS+bVMT37vH9pgvbRxVm6f9Tr/pU9FX6Q4LeGObvsj0mU7P7v49lb5N8ovSx77OCZ3aMN3aOKWpbJqP99A1bqOLfXwEvesdT+l6Rx8ZpCLteKKQ0yNoaVHW0dFaYPzHgZeXl1ennqxUODEpA+5I+51GXPjk0Uo7BUvcjvtyYsxzZzY5KSYrv8449uRmf0vBOQDGPHbQqIjdz/q2PE8Cet31jqis3U8He6w8S6FOQafu20GlanMX0CIPLs+++9HQ6tuvdu7f097krH8E3aonH/Q/moJOkzxvmZcJ/Lt1n3RwKjvp2lp3pbuo+1gXg/q1ZvneDRu+af2mjYGrwZyTvJ0OPq3LdU6Bu6lubpoYQxwwosyMKy6f3oli/EqYV06dyXi0czwtpy7wxGCTNybSOzWSU8Bv7y4no74LStV3naqofhuLPiIg1ekB2gKVj9+JLPv3OBoTv/cs98Ccnrpggp3IKUDtsrf4N9QXnR6gDuBJJdq16RHnwgr2wTqHvg3vOeBUdRF/bJs7UOHAix8PS/6G+99hVpIRZTU56faROtzp5kh985Ps45IRH6G3HIgl5pvBnOnkK3lOepQ+VcKnrq/sx0TGgRR4So/WOfiUTkB1fk4XEJ18LH58OopySH6Q5cPx6z4sO/njnfxti/idyFMZl9+1P/Vtykfb55RuDjz9DtopC+bbGaxWbBTcpGxK6ZYC9gv7vnz58krB1DUfEaPCtwInPzztQwCxHPjb15aDAepEwXeg5rxs22VO6mX6Wq8f+0jt+/SW86XglAGyo1uctOQw+n6nNG20nyjdpEjZRqeAizfOty7fn0ST0fceA/yqMfkw9n8/3WsMdlhyMueoizujgcGjulf6jPq01vnT09Ordz+VkZpO5XQ6zzo1GSydrj+hhLOJHLA5rXtKd9+q72W4ORhHzKFjZwxy3rrX4YwNx7pm36uOxHeydZLj0RnOFeyptBRwSv8UZFxJv23Ud0Y3saYwisT7xCTSLTp40gFX9UMaxyt13RKQ2NXzwJn7k2249zjra/UbEryu33z0ym0k/8QBK9q/tsXTI3R+BJmYY33nIFNhEH0ftkce2CcG4B0QO8HUTr6U1XQ/5Un5dzx0wYW13m7A278jGW84lvT9zOMUeEp5iS/ld5oY/EpkPqrPtEv47deFMKjUvTuQ94lXKVCUglBJxvZziIUu0/lBXr/219N8cDmW972TeesgD/UBy3tsT+f0jr8UH6HOurJu/4jAUxcoupKHypWC7YI3nUKrRcwJ0BkDdBz4m0qADgcNWUY2Jz7r+laZnZZPlBaZA0F8FNH5UoCK8rUxbiXL+0kBJwWdHIIkg/cGXpIBs9Z6syNcn+7RByvIdBKqrovvSrfc6/5Vg3UXjErG+JR+ev/WvJ+J/lS+Pzv9Lpm+B4tOdSqdAepCYkXpDOu0dLKmc5qS08D8TJv66zqvPjLR4Zv7MOVnvmQg8R7vWwZrvf4bazoASf91sks4Q5vBvNbvzjmZ2iMl45pG+cnjDwmD0mbIboOjM8LvuXYn/co1ZznvyjjN9BF9eQ89cOY2mpw/pp84iybOm2ledTq1cywnHqd86TRqrVVjjTc5iDXFe8KJ9Oiw117qd/cKkBP8ob/U+WInNDnoV9dXh+Ndm0m+RdU3B6h4nfzBlOagGH2xhFsnhxQ6f+TkFC3z8nd9MwBVmJI244nlXZDkKtmHouxS0CnJ5goPnR7o/PZEk930mei3BJ6uTIZJOXtRTYpnGoAUFOmMVu4ErPX2MTEa/lQ4Ve/z8/Ob9xNV1LAmcz3Cx6BNMkqvGPhszwGdLphFeaYd2SR3jwlPapleXvKLHJOjVOCSnCPKkW1PzoDvJaVNPk8oBZuSo5OUZTLoGeHnzoHL+6RT4iHdS7sEJwZOytMB84mDuCuf5uRnNrKvODoPek33lNOJY3Alz3uwKPGU8Co5AoUFCV/IJ7+pK9fKO1MpSJWC+J0D1WGqd1cTdXohyebEmDpdbynYlsaKjte0S1xte3OkM+A7fHUfyGMap46IOVPgiQa9d6GTU9C9GyrhUXIAUkDtlrVuuXXjfhUrUtnPTg+ceR9dcRYZROjykCYH8sQv6Rxat192L/OeOK88zUSM4UmmSt/VST1He7/mI/VmegVH8kk68ro23qYxmRx63+uumb+rnzY3/bmE8ayHp5qTzNyH7nHF5NNYPvTn0sa9cSzN1eQDTJsUX79+/WG71MvFu9O2Dk4xzThk/6fb1O8onSajvDvymuzmxHt1cWf/THk73XJqP/wq+uWBp04JXC07GfYn9VNZuN60a+vJxtM8pWzT88x1n0qDAaa13r48lEq/iziznap3Ap5kyLufnUNlhdWVsQJ2GfPAvrovBiXKg2nk13I0QHVOQVqsSQ5Jpomo9KiAaJhbOZZStYNARV73mI8Gf2p3ypN4phzN9wl14DzJs3P+kiPQ5U106nDckzz3pzX5oNvoZPxPxv60np0+Pc2zVjZCi4hH1G/USXy0m4/alXHHumjEVr7uWHRyABLv1KWWsetMOqN7hLnSqONtgHftJjmz7cS3cah7pIGnnhIZuzqccR07o7LTox1Zr3vneK31ypCvOVO/08tcvdtswz4Fn/zx6Vvi0K3Uzb00D3i/+93hTGqX9F7H4lZKfKY+POg2sq1/K93qnxgXbENad3DtE0OYn34NdVbZ1dzsLp2w1v9eQP709PTj2/2ob9r3xC6eRKq2qh7/G6jxJ2FVZzeyb8zndbLTu1OeaY1Z3j5wQBx2OxyHkuXET33bRiB/xrfp2htFKY/Jfgb9Ff5OLw13Gt/plN456E2PbsODfJ0GoNjXU5txupfmZ5L9RJ4X/J02da7gVoePV+gevsynf9TOirpzUHd5Kr2oU2ZdGg3HUnIMODGCbPJ7oNLg0ylweuLZE7kz8iflwfqnnQYCB5V8ytNNaPPX7XxU3u79UAxide1cOQnWGai7xTopMwd5mJ8BJCrFZLCno6RUPEm58ncKXKW2S25W4J1St7ySY0pZdPNipwRT3Z+dzOefwveD9vReLDrFmUq3/i5ngHkZWLBzYIdg4jU97pyCRCcOvNNT/zr9Xnm8q8v2J8M//bbxV5+0gZL0InHZn0lepiSfDtOnQH/CIfPkYBHxgDvQfgSi0h106gx+Ox3Gm+oL8STRrXryxJDe6eQJZ+5hZH8EPXDmfpR0RZdncvCKbM9O7Tk92YwnjvOOuBarXKWVfqOeq2v+gVIFRvhveJW31rftetfHe+mpDm8sn1Lnl1CPdq/kMF/p/oTjHa67zuKPQSkHfchzyrPW64MOHRHPUiDJ+EcfstucIi/1u9K6wNP0qF0FOB1c8n3W7zWR+Ol8HQbqOqw+8XkmbE5y6zC+6vY4J9/zVhxKdV0pa96m+q/Qp37UjgO0M3B3eTon4YRHKhE/EsbJkganFCwHkWW5+NPOaXdk00Yz79uA3zlMSekkeVW6H49LfWY/E49szwrf/67Bd18RlNK4uG4r4AQw5t2ytuxshFg+6VM8JcOdys7Gv8vQGXAQqxxQ1ktj3w7V5Ay4jy57SpNRn/K9N8/vopN+fka+PxudyOdEjveW81Us6n6fOIs0UNd6retp9DsQtdZ6dfKp9KJP41rv0amgPk6bBZbJbp53hgnxIxli379/f/NPrqdG1GQoVT+IGcQb98O71CmPMY1tdFgzOT/sh/vldphG/Z8eP6ABnz4pT/32Y97pwzaLD2/A3IvS3EtrtMtzte6ik2DAR5LnywNPbqddAKGz9WxP7uhkXlYaHV7zsGsjBRisMxj46AILpXefn5/Xly9f3uSlXq4+ffnypfU1aMMTiyof7xVvhUl+KfpuXVtXM9/ks5g6+UzEMTMGur/0CVnG4+j6k0/YYSN9TvepO31V/qrb5zzyxnXNm7I9iAd1wok+jYNJ6VTUhEf2ocyT+fwouoIt3fikdd753alN1jnxc6KnJp79OO976VOfeLKi7gakM/hTnqlc0eQg8JsOApUmv320lXVaUdsgZnspMLSTG3nYOTxU3HzUwUTQ3dWd6mB/WQfvOZhXYJiUrB/LYB4e62XeySFIoME+2BhJfe+UoZUy67BipaHPOtPOgpUw75sXKmMbOf6dKDk97P+uPOdPt+4m+TPPRFcU5C1Kedee+1B57tHWf51+h7N1C87c8rv0mA3moqennwGnwhz+c9mXL19eBafW+p+jQIOflNaTDVRjQyqffk8Ywj6TOtnaSbFcEnVtV7nCFD/S7rzEk64vNva96ZOwZsdzMjbpnK61YpDHmxNOT4/VOXDkx76Z3+nGGvLDttd6/592WJdONgqvu/k04cyfQB3OPOgapXXH375Pp/6Kw7lzLic+pjY6mzb5LVWGgX/qRAZFiCVdvX///ferd9fy8/z8/OpxO/pNSefaF5hsqJ1/WPLkPfo3Ozn6nvue2uJv8lY4UvlKZvy388Khbj7VOJWNQJ/Qfg55SPVxA6qbl/Zdqpx9ieSLEGvSySf6OTtMYrm11ps8btN8eWw66tZK9dtp3Rhx7L3+k4y7NlOa76U2d9ThZsrHOZvkeCvWfOrA01rXg09X6+zaMXEAmL++qVhSelEZilS6ZQBTkaQAE/N0DgSvrWwnw+pEOSfn2s7IJMeUnupI0XwqZS5gB+QMHpSJFXUCmc5x4rcXofOnHQEa5mnXwMqWjoAj+52it7Gf2mYfprFNynvK73FOYz8Z+Vfyf1Y66deD/lw6wZldHuND0snderPOcWCddZSRxkcZbIjSwSid6GP8SVd7x7vr/04WKZB1Qie6IRltCRc7w7yIWFE71ebF38SeSvfpLdsR5rUzSKnHqw/JCeAusw3zzri38W6cIR4l3Jnwwn24t168VdeezKXJLvwd9MCZj6PJzrni7CXbcudjcF2c+DPJhk36rOsD++E+2V613rNfQ3xZK/9LXZJJlS396rT0+wrGWNcYY6eyKUDQtZF+s/4UVKogX7WVAhPmpzCT2JWC+cbvCWOmNu0zWO/XvQ5H/C+p6V9TU9DJvlDlKZ66YBN53RHnarch0tVzIrvU3s5v59pzO7fqePu3V8qd5L+Fr+PA062gfg+64oB2xvyu3vR7ylOLjQEPG4Vrvf73iMpHZdEZ+1zwPAllRcZ2Uv9oZJ+cliqeHNDp8lXdu7yJ6AhRbl3U34GjVH4X7afydp4JsPnbTiB5q/vJEE9KnMrcyj1dp2AUy6X77oOVdPXdCjjN/U5J22g5NYanfKd1XGnnin64Ur/buXrvQX8evQeLTnUNf9faLcyg7vKGhPU98cprvR41Szog6cb3zGEbWifBJxqIJJ82pl627JOj0fWZ7ToPj5wn47Aj4rUxZzKY6/cOX7pNibQrnN6bkQJUTi8+eL+TAzHGsqWt1Nl2O125CxjRiE/y7OpK96rce+b9Pe3nCV+To/LAnEx2+OqaYz3JufMbJuey46GuTzAltcE5caqb6Jt077xxcIsbGjxt+fT09ONkLW0t80fbvtqucsQw88Y60nWiCUP9WF/qb5KDMaDu1XU6FZvknHCKPox9BtfjcSHPLGd5JZklrKS8+NubD/Y9eCopYc3JI9vdJgbbcUDUASh+El4lvE84VbLhfKHMunXoetK15T/N452udz8mHm6hZK+Qh6sY88tOPO2UxAl1RnzK0ylo5uuUWGonCZvG6Fqvo8Pfv39fX778FG/l8/s20rs3eGyVyrn4YfDJkzoFAKb+W4E6fxfssQJMY7CTufm2cu/ITpd59b00ztUGT6B1fbERawPb+ancaLR3ynAXXOqUZ9fupAxS3ZZTIsqQRoLnGmWc6vtII9h1px20ZBDdmzqg4Rr/SDk86H/0XrCtOm6tx1jEwEPKY71io7jWbD26UIZdpZXRRyP/r7/+evNPeD7dxN1Yr5NkmFvn0ZmYAgKdA0beLDMT27Fepm5LwSjK3P2YDMIqU98uZ71SOtJEvPa7S2wQOxjGIE3VZUM7nVSykc58xp9k0Kd/UCWvCWs6I/mqznPg1PWd4lXH472M8Y/W5bu1UJTW3QNnerKtkpwqj7Fl3AUorDdZp6/fO1Zcj9bDXZ204Un0Ndwf+iekb9++vXqcrntlCPOs9fPJDuuUxCflbLk6EEOZsH36WfSrEk65Ha8pfuhT8Jv+jPlynx14s95Ktn2aY8Y1P9JInjvf1m3Zr00BIr8axK8NOXlcO2GVP8a44s+ySv7UhFmWIW0syspBS8/Nq5jC9ZqCYRyX07o6m8t23Y5si6U57HZOddiHBp6suO/hCJy01bV3ksfpSbhTn6i4PXHrmsYl6y5j78uXL6+CLxzUZLxXnc5vfhno6vrG3+wjyyY+Tsd2mpisOylPLhg/eldKuPj0AkvXNuy7YJcVPssl48EKOgFr2lGmcq26fN9BLLeZ2rLCNaXg2a+gzhG9Ne+V+h70oI+iDmd2WML0uk512tDmLiudggo4VVnuUvv9EkU+RVt6ig6FDRdjg/sxkft4QjaEmG4noOpNdVeaTw+zPurQLo/bTmldYI98dca0NwkSvth4L+zoTjzVfRv0CY9cPt0nn5MBfW+6Ny5MeX8lvtzLVv5Im/tPp0k2XQCjyOu38yN4vXN4dzxesbGpt6Y6WYZ9LoxJf2Cx1nqzyVHXJ09I2KnnNzdF1spPUST/if3o+t3x0xHb2dVn+9vtVHAu6cbulDHlSb+HGFx5mGZ8TliT+LQ+rzT2L21YpGATsSS9r8mBoS4Yxft8l2WSN+9Nnw6fpkfuPJ68fg8mdPN1she79ujzJr2RxnuiU31jW/aEPuRRu46Bjwbtk86fCsgGtBeyJ3vlZRs0bLsXmTLqznc9URGnXQCTg0/sw5TnhEq5uSz7+J6x5YmxkpGdCAfNUnCq+OR4+PEGLk6mJ9kkJZTGPSm+dLKIv5MSTwryxNh3UKmbn+5D1zePzUc4Ep1infK9J8+D/nt0L2C9te2pfhvsnTPQ9aF05VqvH3tY67VTwN1r6requ3CHmGX96Ht+FNo6goH/E9nU74Rf3Xo3f+xPOu1EfcgyyUh3XVVfYbXzsD9VJhmBXWCMbadNgPShIe5AE/PY0PfjdQmLXIexyGnsT8LCj6BkTCfdfy+cOa3nHrQLNuwCJtYtD7qdJjuq0pP+8Bid2mBr9cFs44Xb7fh23U4rvcbftKPrCQwHmwpfSnfzNG2VT5sbSWdS91XfuvcfES/Wyk8rTLhRfkLlS7bzFdsgbTBb/7M/fBKG8uyCRR5L10usKnn4KZwTn6/b4Ej+iE8REWuMKSno5GCV8zkAVSeq2F7CxRTw4r3dei757vSmT6hx3EwnOjjNO+vwU8zZjfU9MSHN2RP6kBNPBr6PMPBtBHRtc9Am/rrBSIaq062obTyWY8DH6oonKo703qZOfimQYmWcjG0HbEjdSSHKOyn7nQx35DrZP14zXzlevueydoistCmfJBuDUn0nQLCCc5BprRUNeX6K512eBAa7IFVStrznANZHGNeTw9AZa3YKrua5F88TPQz8P49OsOnquO6w6NSprTKl52go+/GE7sRO3a973HHlY3ist+6Rl2qfGyHUzfy9Vv/PZenRVzsDCWc6SnKjHqchOvHTzQHWUzJwXS7r92VVX6u95JBMxrD1sg1xG/HeOfZOcQo8GUfoYBqLbOSTp+SoGCNZr9NPaVpDHT6c5DnBmamee1NyQnzf/L7XFnvQa+p8iLpnSvaN8yebMtVDHWJd4UCF67ItR952mGQ72CdlOfcLE6jDizeebPKjxX7MjtjiJzfYN5+sLR7qnsmbGazX7Za8KeO0yeKAWKcnmYcBOf7TH8dg4tNj52Ab+5B8oQ6TSdbLxM4OX3htnHHwKG2CsHz6TsGuzsdJn6qHfUrrggcXSo7T2qTM072E4+k6jUHX3kn+E/1/Tz/pVr/rwx61s9H9kSDt+jvjYpeH96aya71+jjdFP/m7HIOXl9c70CnQRCOeSpMKwMqHTkO1aX5SoMXkoFUCJC7OLt8J0QCv6wQgHSh2zzbvnAiXI/8dmFuZEDCKHBSyMc48SXk6OGQj3nnoPKS6WRd5qrzu4+8wVCcjre4np2Cq72FwP+h30g5npvk8rQcHe6yDkmFJfUYcMja5jA0vGxaTY8B8HWYmDCsiJha+JKfEuti6kgZw13YRZeKgng3GkpmdgjR2HmvKPWGNKfXHY94Z29X3tFPs/HacUtspnWNGTEr94Bjck3YYcpqHeXd1fQaMsS6Y8n0Gfj8jJdvVeuWWOrs1cGK/7KjTre7LiQNL/cW1mezv0nnMUwGUeqKj0q3Td/1hwMQ+kHlK+rio0y+Vzk2W1B7xJgWCOkrBfvNu38u6lb5Q6idl1PlKflpj8mucZlyhj1Jp6fUg9an3ONm38UaHn/BIQaRUP4NVHX9dHe6f8ahbUyb6+lPwaSLzkOym7nenP/5E/f6hj9pRMUwCvpVcfzLwEw8nv91f858mMhW5DWVPcp5+cqAoPd/sU1M2lqm86mNDnfeSAd6BkI1s81z5TuZIAg4rSPYr3U9Gf+W1jDpwSqerkkw8vhxn3mckPRn0Bhr+pnNEhcr8zsNn762Yiw8GK7s+2LH4SLpX8GgKSD0M7Qetde5k3qsu1tfhiufsLrCanCIbp9T7pnpXYJE3MZjG+rvdVAeCOvkkQ67rizGMuEY+iQ12uq13U8Cfus48Jt1nXtIGUdffyUmyvTIFRRJW0AGw4Z6cg1SuHIXO0bDsknFPgz59s0zqF/vn64nS2unynNRzmv9KvnuQ184ub+JrsmkelO39zsbrZHhiL3b3O9oFl7q5kXAm1dPhRVEKBFH3d336/v3n43bGh3q8bGrXf4LBtogX6eXnSR7us586sf1f+pE+T6qvC9xbN1fd9g0SfqRA0NPT06sTtPaDfDon+YBXbJyEAQ7cdCeZUqCI94hlyf+xD5V8Jdeb+CbP7FsaQ1OdZjsNJtk+ukrTep30+T3oXphgG+eU/thH7bq21uoV9tXfk1A7sCpKhmsK2pTCs9KtOtwW04pnOwLkMTkeJ7SrO+U7GWfLMYFpUQoSVRkG50o+lHHik06VHYpuniZ+PRaVTiVp54B5WI/z+Ts5Hp5ndqpOFW2iUwfgVuocB+uLZBSe5HG+e/I90a9ySB70eekEV0y7gBODKzY86z51cOFI/csdDf7S0dy9dtv+91Tmr7w2tE5wJenXSvMpLJ6ossND45B6uHMGOr1Z5TrsKXnZELX+96Mw7qsdgMIpy6GTWYcrUx87p6Du+SWtyeHoDP/kOFi+KSj1EZhygiHvwRmOQ1fPR9PkVE+6455Oyr+V7KhzvlLndjpikm/yS67QNH6ndrz1EL/rvrHFbTOwQb1LrCmeaEvb1l3rJ7bwMbN6R1RtpFrW3gTx+wxP5ELbnzLx+6e81oiZRV1QxfY3r1kPfZWSkfvI/jBY5o+xhI+LVdudTJJt0gWIjCt+V1OXlgJPxi9/ukfxOKfS+52Yh336aPI4JJ/9PfUmnb/W3DfP2TSv7km3yPyXPGr30XTS1i7P5CRMzkMXsLIiT4GoIp9mYhtU8OkRBweIDKBWmqkO9iX1+zT4dDLpukgziX1zkKja8kK3A1AOE9vonLX02B75sLNBPm1cd992XJzWGfjd0dSktGn8dyCYdgNsIHw0pXk2zcVOiZ/kedCDfiVdxb2Uv3N0mZ42IKwbvWtc6XUSym1bZ7JeHzG3DiWlYH7xbdyygVV9Y37+9sYNy9ggrXt2hMwn+f3+/fv68uXLK+yho+D+d+NgpyGdLKtrpvv3FBAyJuw+CZsS5lBWzmcZG3N2AaZ76OgT2yWl3QNnpnz3psl5MC+cTw8sPKNORpzfU+DJ+Um3jkHCglPqfBampf6kNsrv4Lr35nil8VG7evyu9OjXr1/jy7QZ9OH7o7yRnPCrs9cTdrFs1cnAjQNCTqtNF+Z3oKX61OFM8eYAE3E18Vz8euxYtqPkS5omrJkwpjvhlIJMqY6EL/SHJn8p4brx7Bbq/PZOthzjnf9765qesCmNrX0/98t24j0o2Von9GGBJ9JOIU75T8vcgyanwROAk60+NE4d0Kg0G+ul3KjU6UD4fjrGzzJcPEnuuyBSIgd/kmzsDExkOZsn9oEKN4FQ5wBQMXM8CCoeCzsXJb8UrEkKkMc/d1G0P4sAANJ1SURBVIGiKZ2/2cYu6OS8J0rAxu2kRHYK1nWc0KRQTxyBX2Fk73TCRA8H4BpdAeVf2d492u0MgrpnZyAZQ2utN0YPdZn1EY0NG3tPT08/HAdijvPznl+s6jLmz0Eoy9v62AZ63We7bN/6LgVbOmcgBaKM3dzlr119n7Z1X5MdwDo7DLbhOOGC06bH7zrDv3MAnF7fCWM6Mobdi1ifMdL3SbfgjOu90pd79vsq/nQOyYPeUjdvdjYO8111LG/xgep3F8DeUed4Gmecttbrf5v2Ky0qv08h0V+h33PSb+r1ZP93dmn3249xM+DDb2NbklfSqw6wmMfCDvsZDoZZTp0f54CVieVOfb20ETFhqgNO3Ttn6/5a601gqmTT1Z9wjn1OfpLlQHkU+dBCys/5kGTVkcfF88BtneqPZBOke11/0vWOTv1H2yUndBx4+lXBnx2dOL8n1Cltp0+g3yloBz1ooFJh1wTliSfWUwuRuwpdcKrK+W9RU14qQDsJlq+dixSA6hyQJMNu3JKzkohyJECYhw643Ecbn0kJWBYc70khpkBRUtxU2KcGEJU527RSN5+JJhBL6QmQye90f0cnxtSvMOq7fDsln+69p90H3Ua/Qr7vDULZ+Z3yVp4qx+BR6TS+PLXK1A40Mal0xpcvX9a3b99+nICiYc5HH2w00wEhf5Wf+ni3GWEcKV6Sc0R8tXysU79+/Rp1IYk8dkZ+6Wbe5+Me1HUOCJLvklvn0JiSPq/0tOPMMpNcmMdBJeNQyjvhCOeK+e7yM/A54VCSj6+TYT+VP8nTOZLuR3evoy4I0NV7Gty41RH4L9Nk4yQ7su47T5ovU7DHbZ0Q9ZHrTna3+U7tU986vU49lV4gltQarxNNXvO0u31tPtdaP3ycqv+vv/768c6oqr8j40haV7T/7bMxcE9MTfrUdnxhDdPNK+VSfLy8vP4nWeteP6VR+aosZV6///rrr3hyLNVF2bivvP/9++t/Ue38mxQ08gvHE77Ufb8fy/hGjODaY0CLsqY9Mc07zlvWT5tooiTPDjumsskvTZieiPnTWvBpO7eX6knycrrt2FP6JSee/k1EQV8FDBq6Ntw90f1InNt0GeZNlJwcLyjXlfrLdtdab5TgqRySgU8+irhgvHC4s2x+p7IM0F0dw3TtPFaAp/UmZcx012ceEk8dD7sxI2ilOk4V8k7pXjHU72FE37OetR67yQ+6nToQ736flLd+nYyFMvT5eEQFAr58+fLqb59ZZ7dhsdPrlUZyPclArHvJAaz+JKN2FyxhfSnwT+Pf5Wnc8dE81+/NnrV+vuNkosTzSZkT2uHYibFrR2oiG7tJll0dtj26uj8aZ9KauqUe8rwrP+W/F4791ynN887mrnu32v3vyfOe8rb7/Ztpld7Z/bRtSwdWoKN0bgWiTuxf6sbSw/RBHOBnX8m/2/LJsG6D3s5+5U39ZVu203k96TLWmbCG1M3B4p98OrCUgiZT0IG8u0/WMwzyMG+Si2XL+liG+ZxmWZ1St36v6PG1bn95+Cl/7y3TBdTqerrf1X9aZofPHf2nAk9JASeBMj3lOXEYvMCKqPxqUvg6Hc3vFHSnZNKR3FJQloH5SgrQj6EVeUF6YbsNyqWTMe/7MRAHmVhPKeMpCFV9SS8np1Nzapx7N9rpk2LtfndzZyID5OQsJDAtqjnCUwAf4RicOKlOf4+xPc3LW+t5L08PelBRhxVp7ls3Jf3CDYGOuNvqb/5Vth8po5FM/Em4mbCzyvF7rfxiTjoDrs+ncGkAO22SxRQ08rsXq34G5LjDXLKo3XrjSnqkI1HCAf6mLjcGdXWkNqZ8Xdr025QwhBjjvC7H685Wc9nUfvd7ao/1TjbhtGbT/XS9+93ZRw/suQ91cuTa7dZrZyveMkcmp7DypTY6P4Xt0c84oSpPf8B+Sd03/8SSl5eXV09iuP61fp6kIq+p3HRyo1sj9bvDKOv1SU7kzRgz/Yu0+8C2eYqJeFdyqTrIW8nG/krV6zY6PCa/qQ8dRvA3TyYZg/14nfHYJ6J2vlEiyqDDFuOPy/OkrsfJ6+jKKagrWHNKJ+v3NE/Csh1GWf8kfbaj48DTqXBOFOevpCQQK5+iKU8nWC5up9NIpOFZVEatDVwHmtKjdv5HCfL79PR2p6DyVVrXj+KX+ZnGsp643WLsHp27aoTZoTIvk0Hv4FzlSTv3JI8rgZH3qRypMH0vKWaX7Y6jOm1H743Sd2ukk0l3n3W5PpbZGU4dT6f832qo75Tvgx50hbr56HXQ5eN1MqaoEx2g8IteTWV8lz71uzpInSE24UzicaLiO51edcClM3B3+pJ8si/um52CpLO6XWviPNua+m/DmzxcvU67z/7dycxldyfJknx3+bjZ1jkItLXSWjnF7ltwpmtn51g4PV0n6tbHru0HnRPXgNOp26hzbq2n6L1Op9uZ6prqr76lNeS++rSm/QPWwRO0nu9pTlv38trvKCxeUqBrkhWxovN9LI/upGvSo9zoSL4By5EXHw7ghkbxU/6B5cX++zCBN50638395/uZJp8lYUv691T6OFVvCtqloJNl2mEP5yb1e3d62jZLWp+8Z7uA5Slb1u32SBPWnFCHYc7jse50mOdBtzbJ+9TmqT77z5x4SgbDNCmmPNOk4f2aqFTQLkMl5B3Buua/R5RCdKBqrdd/NWrFuVO+KT0FLnyks5PFrh7XyQlrYHEfut38aeJ3ZTnGVwI1nfG+1usofgKoZPyn9LSD3zkE6ffE62lfO+dyyrtTQPcwvu5Btyj89Pt39uFBfz518ypRmnc2sNI1H4tITkHxUXXW43bULXycwvqdmJCCUKkfidfiwf9QRD1tDCoebLgmXWdZk8cUWKvv6pe/nc+GqB2SCkDdgjUJbyYMcBsJW9JcOzH0d79pwBdNxr4dqW4sOEd3OvgeOnrilXwVb10duzZO0jnPuB4e2PN7iOuR3x2d6PYTol/h9ZD4Y9spX3Iwrfvq2j6I+SBuMHCU7NYi6l+eMul8lm6TI/WdMq/6WUeHV50tzb5Yf1pHp2/K3FjR+WT+7p50oT/GvjJvosR36msKBBFrGXSafJwUYEr/ltpthJyuoe6ULXE//ea1sehE53Y4l3h9j+7ubCenn+qVDm8mfq0fTvtz1xNPpx28Z10nbXmypMnTKV5Odhs/zD8Ro6eOpNb94oGKY62fL5PbnXgyKFiZmueksDlxOzBzvd2k6xwhUgKSrjzHjgBVvCbwYh4qkWTEUv4nZIU4KezphXreJejyVfop3arQkhKexnZXj2XsPKfOgq+v9mla+6d92TkhD3rQFUpz8orznHDIQSHumtZjC9N7NGysVh1rvcYx1pF2zl1HRzSGjTvFNzEp9btot2NK3tZaP+Thfprcvv9hNjlHNvYd/KLsJkq4YjxIhnnqb5fPeXblOj4muooBHVb7mvx8BM6kNdbZftNaddvp+vT+lbYe1JPH9/9v702XI9dhpFG5uyfie//HnePl/piL7nQ6EwC11GIjIxyukigSpEgsSUjF+ljNMf6s1lzWVvd7B9gm9kXVq9YPxzZqnvO1KrbAz2w3Qk+wbcK6OT5C2Vm3ctySESoKHDO59Z3FenzvHSGDZZTdVv1DoJ3AMWEon52JE/zPYF2uCB+e57xx0SnH3xUR5a5DGTvgMXF2IdvkyMoEFDG14sPtAc9NF6tj2W7M1LE9qn/xv9vOj8h4qpyHvWX4GCrr1QAXlYJ67C6bvOzgsjPPhiAzHh0opc9GOzPoFdg4KEOg5OfdEpQXF4f7Oexq4aAMbHQ4sMic/I+P3rPU2XHV3l50lWr2vXJ8GKpc9CULBKJMB9ma6c7FFbkHgz1gp3Tbvs6rjsPiiA4MANiuYDYtbmagDAH+NTflcHWdD7QhKGfmiLOt4UcVWLfGced4uiAnrsP2OUsM7UxcG5tErkyMtyL3lMPnnHo8h1DBjuq/Iq6wDF/v2qmOZcic/hgzZ1+wTfe90vtdO9Op37VXrYPMye8EACjXYB3sV8cx/qzurVsjyvfJfCI31xy4LSe7apfr2dMe+9IZaYT6T/mYUTf/QjWODcqIehPPVdk8fI3Suxw3qPpCNrYTeD2SKQ4sCwLfaYXxjCvz8fHvcdDX19e/P3ThMnsyf95lGbk5x/+jjuy8qgPHDcfOxTtuTFVbXKaTdevGxxFQK0+DqM+Bjh538bVby86OdD6zrCij8w26cdWPIJ62radoV8tk5TPHxxkLDgz4cTkVWCjCic+xk58drxYj9hsVGQcFDOc4unIsFxs8Nn4slwtalPFxuwrVWDiFyA59fFbXB5Qx4+MKVRDQVdyM6C8q1UoxOkcrzjkZXXm3hvb041HqGQw66AYmPA/DqUJ9jE42fkYSBB/l5rJY3r1wHG1VN6jBukN2PMY2g+0CO+BsB5zzjGPF7cVxJJpwrFg346aH0vVISHHmWEefZDYDj1XOvivnyihncyXDKdB9rJCdX2Uf3H++Xs0ZVS8fy+xW5vcpWTq2cu9n7tdgH1SQxZ8z/0qtEQU3X/C6FX2ZgWVy/nZn3ju9yfWrjfKPj8+P2sUj3lg/2ioksFAXc2Zu6OY4pnx9BY4h1FMg8T2yhHk8uO+sT9HXVnbZyYRxCz+eqHQhyxZQT8HgdRw/uUytbBOiY2e64LhExS1uDmZxpjqP41g9gsfluW513tk5t872xhDKJ6rmv9NzXVsTqO75ig47nXhaHcxVhXuFwVV1ZhMmrtkzeZQjiWPABFIoCEVIYfk4h7+k49pg5w3b3ra1DCgVNEQdmcLIDDF/VoSZCkxYblQmvJMef5iCmjn5agyr8u5zpsyzz5ki5rZWiCaXLqrmdxVIZNeu4ow6zqpH3c9MAU9gsA+Zsc+OOR1+D6w6F3uDA1WHcgyzc3Etkk4O+As7vMvMdgn7U90HLKPsiHKaXT3xh31269XZHRwPDEYwQPj4+PqCUmVT1Q4rB3VYL8uZ6X03H1Z0T7esm5crYCefx4MDgqouNbedfEd1cnb/jrSR9TPzP7mc8wvGFvXBa5fHzvlxKwHrXrnOvNYF53i+8nG4HD/9gD41rvc4jr45/lc2JM7FcWWrmDhA+TCG2LbPv7QX54PMQRvndDJ+x//Zr9qpYJ/HIvofJB2OBf7HzGNsj20jf+bvGPNEHZzxhLJye1Us0oVrp1PeIfMbM59K2aAYK8z2djGO07dOj1T9quyb8rf4uLo2e62A6pu7366dCm3i6WonXimNe0A5kGrSKEWCCxnJDS7HyieuRec7rmUli0oyynDGEip5PK6e82WHG2VS4EAAr3NGI9ph5Y9Q9fHkx+v//PnzJfDBelERc79QTl5kfD+5XdUvZYSytFVMx8U/PB/t8jGn7FVZNYZcl3pMkYMpHBceJ+cAuMCvUk4dY8/tdpykI1DrXylgFcRmxnvQh3MqHxmduZ5dw4GQKuPWJeo1ZQsC+B4l3pFG55Yza6Md/BWeqIczdTPnzMmf2SLeXVfj43SyAm5o8HrlDZ9t+7x7j3oyjoWTGnKw/eaxwD5XxAKPdciJNgTtXtwP3lzJ/KtM11c7xXwM+88bQZEFFu3Ebn98x400DgYyu63utVpLK+tTOeHZNWf6r0onZIEfB1b38qUfHWyPld+v5oDyS9zaRXTOu8erlH5ldO1FZSuUD1zNo6iTiWV1Ha/N19fXT31TGxrxh+/l69gZ9Vllusbf6+vrJ31XPcmxbZvMQHKxi7Jv0Sf0vQNM5qFOZ9sdbeI7CJUNwrF2wBiS9c/b29sn+4bXcLn393dLFMZ5vvdhyzhedYRQlq0Ufed77uDiLIx7sxgM+8bkIP5HWXiu4HxUsUdVJ953p6u4HTzv7CfLt23bl3nQtTXf8lG7jrG9RRnnFAVUcM5OLpZxjrAqg22gElLXZk4cQ9WvjHXFXKsdCCcPHlfX40JAWXjHABUVQjn+nT5kDoJSYHw8g2o7a4/7wv1DBa2UtSur5FTBhYK7lyxrNhbu2r1lV+qr6tm2IZAGj4fM2cjOo/1AMgChnNVOEMT6nT/j944t6qw7pXc75RHKVrDTH44/7nhnegYJvm3TL1hnuZztcMRDNT5n6S1HPm3b9sVGYCCB44OkKNodtNVuE4idb2fTsj5nc9KVG3w/dHz57vWO9Mjg9Gi1njtzt4Ly4dVnlJX1HNsPJEtCNwbRwjGIIidcHMAbD67feL0Ksvldr+z/7xlL9vd53BTxEH1Hko3LIzkT3zGmY5kzUonHpZKf4UiRao5mMQDLprLiFAGFtsRtUqGdYZ+EkzzCjvNc4dhJwW3MVGDbpXRIFUetEj8duDWFn/fqm29DPClHV03qbfvK9Gd1rZRRSgXbZTmcQmJySBFOSIy4X9lBxc6KXhE/1aSN80w+ofxq0WTlUF6WB9tl+ZApD3B6KtfJRojPR1uZ88BzjBcfK+7MEHUCIrW4K/IqU3hubLpEVHYNBwtxvVublbJ0gWlW7kiZDroyDa5FFXz/NCjbg+ss0/ccJHDZAAYNuEsb9eCOpWqDy6s+OIcYwfatGpds987Vh3YCxwWzh9FJjmOBGIc4jo91xHjjeCmbpGRGm+L8Cv7e3ezowtlO7gcHeThW+C4Ttit4PgKCGDdl25T9VYFxNe/UZ+zzM+r9Z5HzHqgCq+41cRzns/IdK1Tl9txHpRNQB2G7yj/qjAcSEWg/kHRSMQb/SnfoaqUv2XcPXeAeEed4R90TJiTQjsX/zr3L/H8cIy6jdFRk+2AmMdYRY4Tjg9ejXUa7zpsdzt5ksamyOVzWHcOMXNajrKNVdhJnb2FGFPsM6MvgWCvbgRlW+CMr3G9+B2ZGPmFfXDyE48TzDvvt4GzWmXA+tprvq7rpYYmnlY4oxcl1qMB3Txmuv9u2WtAqAyW+o6MbZaMu/IyKPcDpqGwQ8DEBrHvbvi4cBPZXBSduQakxVItF1Y1gA4MGJcYBj+OYYnCB16ACw3aUMlNKg+8rOvmoaNV3NzYu2yn7rsaKU1Nx3FjJKbYeZXFKPsrj+DhSisurayu4NcllqrpWHPJO0HGF4h/UyNZRB2fct5V5dDWcQxvnVKYornEmBDB4UC8gV5sZnZ2+zM5wP1Be/KwIIydDJ5BSNo37qsYv04eIGEPUrfwS805AoOxJZwOj6v8q+P5kjzTw+8H+/PnzRXYMPnHn2WUpu76o+a8C2KM2YsWGdNDRD90ybt0MPqO6v66ssv+sz/j/ERk78yyLZdzxyrfBwB2vcXoK1yk+Qhd67vfv338fVQ5b8vr6+vcafhIjvkdZfvk2H0O5+TgTSdwXtofcHzWGOL4qHnB+O2/eY9yHsQzKgTYJYx3sC7YTf/xOqKhD9YXlxDHI+qT6jn1T77/iMvyZf0XX+S98HRJzGLMwGcnHXCyI9glfpM8xH653lB03oTKoviAqe7d6rgs3p6s4NsPDEk8rUIsuI02yMtt2LCOKHR0EOvIInPxcn1oY7OyhssRFzj+PHcrf7UCw0lVg5c3XoALPyBJlqDOFpNpRZStHIsvWysooOKcfFTGXwf/KwDu54rsKprpQZBQbZjyPhj8+oyJ35FZFyqj1k61HlIfrdOutsya7DmHloI1jfx/sNXrfDd2+q6xFrIMdqW3752wFUYA2jIkfRwYxnJ1hMsrZYw4YlP51pJNyjlUQpWwNO6ZoQ9km8jEMkqJt9wuBmd9ROfksv1sje9ZLBEeZj+DuO8493L3mwICDBwyYMCDh8srmZHZG2Z3MR3S+ytm6/6z6zgg4Bv9QrR03j1bupwvoq2tcrJH5lGrdKB+HN7m53W3L9aD65VS1gfH6+vqFDIn4hfvCegKvceOS2RyH9/d3SWphP50Pr+IC1J3qPI8d6nqOHVW2F48FyhPZtVw209cIFdNEH1wcxOC5qORh+Z294VgEgWsPbQ2PaRzLXlaPdcTcZbvFGb+ckRXy8npkPZHFtJXdYc6AY3DnU3ThfKgqZnV4SOJpxWBmToMqo77jMa5rtQzLphxpVNIqWwgnTnxXBBOTJIrRdf2vFI+bpEzKYF8qwsaNU7YolIOIdeHCV/Jx/UpOpbjUzrlSTup6VMjM9CvD1HFq3DHlODinE5WiU1yr51VGHpdTTgD3JZPZ9TP7zHVUZTpQjtk497dHZeyy+YQ4M2jszIOzg9Q9Mmzb9mldsnOnHL0/f/582QVE54vtjtIfDuwwYRuuj1Xdbn6w/nX18tyJ/qoU/5A77DPucvIxtlVRDsfeOaDcD3T4nSN4lW5SGxTxOeDsB99v/OO6VGCF16PvlDn1fMz5FK4s9uFKfX+2fhj7dBwdW7NtX/2UPQGfW7vZulIyuWMr53nuoJ9f1aVsRYBJJzzO5EME+hgPcd0cYLNcWYAesmYxC//ARhWLcP9Zn6nycZ7H22V5Kt2LujFIPB57bk+NJ7eJMQraHdw4z+xqB93NdJ5/Gfnk2kH54/PLyz/iE205x+T8/if0gT4+Pj6dD3mVzxDj5fQCx1QdOLvI5/faGDU/+TPb4AwPSTytAG9gFdi672eV6QTD7hrFUiKYcOJFsW2f3/UU5x3LrRRPQGVDKVlYYTNho/rJ3ytD5sgURXDwgkUZor/cbzR42BYGFdxvllP1TQUEmYPgFmxXoQacQ43HXaCZkUvZ9049nbWjvqt7zudc8IPjl5XpwK1n1j2D2yIzhlcTPY8EZQerskycKGe3ynDZtq/v6lCkAbe/bV91lSOhQg68BvtQ6Y047hxkRUgE2EaiblO/ZsQbRCg773qybXJ2ywUkR2zGUajNLj7ngj68h5EBhr+OwwEBXq+Cu2ydZ3bF2Z8VW3Q2qno79/aorftJUOPDOiXgfFj8zgFpNyhW9aEM3cBR2YHKn1bxU0dmtS4+PnyWSiDb9I3XgOAvh6KM+D+LQ5yvyIh6Ok84cPaKi+O4X2h7snvMMV2mP7mfmE0bpNOvX/9+BZBtfsQBKtbBecMZTer/EVS+hYpXcCydz6PWMG5y4Avd+d7hRkjUHWVVtq2KjZTMblM/UCVtdPSDGj/lc3TgfKKufBmennjatv4u6D3KqPIuWHLHOECIz9v21SHHc6hUs4A864eSI8AkFwcOaiG5jKmKOGA5ssBE1YNKFv845Tf+KyWP/5UhwUXOStsZnmzBVvOkgnMAQpnjZ0ca4TF1vqvIMkdOGV/l6Kv5WDlLnTJd+c+oZ3AN5n7UtobL8GcVOEUA8P7+/jfzCb9zEO/sowsSlIzV9c5Zd+PgdG8mQ4B3OF0wEGMWxBuOYzymiPaSxyv+WGe7xxFUcJD1C/8fWSvs6KN8ym6q82gn0aGPwCkc/0CQT2xv1C40ysZj4GxVtibiusoWPRqeQcZ7w/kd2Vxyx1SZTqygrsNreP1U12Lbe9vsrAE8xscx8Oa60Y4EOdLpE+sNlMHFAnHe9RXrVpvyamyiD9wO62b23VkPK2C2DI5N6MRt27b/+Z//+UsyudiPExBCz6K8eC2OLctcZTyp+ML1kdvINvLdePG9UfOP1zDWH7YF52fYFXyfVNgVJJBRZiSYsD9o/ziGYr+gC+yj8z/ws1ur+L/TfuXTqfHv6p028dSpcFXJduu9JZSizYLaqi6l5PCcKsOyYBkmkhSxpJQ9PwrhCKMoH3AkEbevDC0zv65+HucVggGZ6ziPsimFkTn6WIbrQ7jFlhkdPFYtfDXfnKJm4BgpAgnvYxxHQlPNdz6v4B7BcMicO9eflXMrZTpYqWePMh58Bc9VPH5W/RVcgHKv++pslDqnguYop8ZU1R3veAp9jo9ARNn4Q0dN6TjeBUS5lF5S1zt7l/VTrUel9908izYx04ltIAYEcS5epsuOv3rZK46BGkN3r5W8FY7OXRxv/sUktrFRXtnSGDN0/lXAgG1GWSatlGzxPetzx9dzdZ+JPf5zVZ9a54P/gwqsKl+vgtMf3XurrndrB8+7urJzAfbps7qydYJllE8fdgM3MxAcRGOskukHFb+wTlL9wZiF28P+KDj71rU1Sn68jjM6QybMakL5UOfGcf5BBxwTvD9qfuH44F9kC6lz3AcE3sPMn1N14ljwj1Ko+6PsT9TDmdjqHiu9yaQS94ljKdwMwnvBpKXbkOPsswpKf6lxXrUxme3Ee7lqY07NeKqUV3Ut4mwjvApWHlmWTqByhqOMcqywPLcVExbT+NX32G1lIojZWc5WQXnYqVcLw90blSHDyoENMTvxGTAwUEwz1427z9hvvJ4NDxofdHSjXnfv4jyWx4ynKIsOslKygSq4wEXPSgxl4bFiMFEUChLHiucgOhHqcROUgY0fnnPZWBw0unI4Fnuh5uYRR105Hk5pD3ro3I8943mmvboHWO84+6Pm+LZ9zuZBB4J3Q7ft86MPaHfiHO9gV7vZzhFXu7jxna9VmbysN7tBAOrmLNhg+8x2Am0NHkObELo1CD0cM+539jnsfvzF+be3t08vMUc9iqRN/OqUuxehv92YIRzZ9OvXr092mPV7yIq7z6+vr3/HLMpHoBpzje8R20A355WfgGuk0gl7dYbyHbI23DG+p1G3Kr8nIPhJUP4vw/nvSh+5tbptn3VVZ307IrojY6wrDkKz8p2yHNOgrlN+8bZ9jR/Qb0QiRGXwqLX28fHxNwMyfiFUtc/vZkJ5WW7+zj47AuuIPxUboc5lHevsWmQQ4xhEXMckSMgb+pX1Hs6d+B+2h3U6j4NCyIJZqWyDuM8hBycX4LhlJBbeP/RR8DjXyWuF7XOMJWY0xdyLsUEbE2UCTCDhmHNcHXW6+AnvE4LjLleObS1fz2XwmJvjLy8v258/f/7Ww76Dukd7YrFv8ajdPdEZ7KyMch6cs+AUSVyPx7dt+6RgkCHn7ygf7yira1hedKbdTnbWRxUgKbjdCFdXJgfKykpRZUFtm39MBBW4Y6ozZ9LBnc/6dgR8f5QiVVkCSPypzKnK2cc2nVzqnuI5/pz18awymcyDQRdHbUinTKaDXeC6bZ/fPYhEldq15l1V1NeqnU7Al+kLVb+yC+g8ZW3hd9UWvi/D2VLXJ7a/7Pyzc8oOpdL37AhykNjRTcredXUbj736+/Pnz6cNMRyjsBdIxLFTrHwKtDksO9ssHI9sDDo25FY6/yx7PjgXlf7tzCNHaFTtZLr6VsC2nR1xfjmuV/51sLgW/WgkztAGRdmwR0oe/M71hlzKh67IdQTaApUogH+OdEJZuJ9xHLNoA9iPsAGcAYVl+X1Z2C62lfkOQforEqIatyx+4HnkZIjjOO54HOXH7GQ8HnIj0RQkJs9tFeupTGf1qDcSXGiL8Bi/iBzHQNmwzthUx7mPvE7Z52C5qmMrummIp51gBetulirj2EK8zrXJmUroqLKCVkQLfg+wA8kOPS4GLI99xh1drDfrV9eRU+1ku+lYJwcFbgyirFLE8blSsBxEcEDQvcfuvFPclaOcwWVLhQzOkef5gGXcde7nsTvInBpVrlNXdrxTZjA4giM2pFOmsjN8TBEYcTz0BO42o0OMzlR8dxsVTncru+TWYEC1owiYjHSqdEllo/ndHCro4KwfBO6MujHj8mr3UW16ZDZL6TLWgVGfIniUs4529tevX3+Jp8hO4D7EnMFMCBXUoq1R9oXnlwtwugQTB6duXLpYJQyO2JmxTz2cNU4dX6HbliMA7n1PO/M3C45xHTOxFMCsqJeX/9vc+PPnz6eysfY5zuFxY9+9Y1uU7EqPvLy8fLF93FdnR/F/1hYS8qiPMN7jeIVtTIwfjhHKx0SNm3dMNuHnCs7GcHwVcrDexfMYv+E4cZ1BTsX/uD7kjg2zsEv//ffflzkWWbfRJvpAvOnBJJP79TseAxU34VhUGyjunnWOcXyvELK7ecx1r+ioH088dQfLOexu0F0Z5/hkRgodLV54qFCRHUclhUpr2/SCjOu5LB5X7TlD0yFqoo4VKJkDygBxOSbnok7VL1QcbpFHu/ydgzmUJSOmugpdQe1cd0mYjIBSO8wqOFLKDXe04hi3o+RFA8SBiPueBQbZeqvGJjt+JEAY/CyoNXPEhmRl8Lz7zteik4n6T813fs8T78xyNhTaCrfpwWOjNgZU/zh7lcfB7ThXgUDWDsrKMqtNGrTLrDNx3N1jaSFPyIG2gnego6z6jO2yvs7mIdoIdT07/r9+/d9jC0E8xWMkeF3IHmX5/Vh8L/B+Rj2YPYHn+HNlN5wNUcezANthpfwZ9mRsUo2VMcr8nDhW+SpVu7g2VD2s35x8Z8LpCPzMcnMflJ2J/y645jiEffTwRTGoVxviWV+UHXVQ+oJ1PdtolI1tDdshjjOyDXasE0mo2PQJ0mrb/j0iH8eiDLbFhAe2gTJyvKLKObg56wgWtZ6ifHzG+YV2Ix5fV/YTs55wHNW943FCO+g23V28yT4Px1PYZ+6r22DpQl3j4m72lVRsi//34scTT3vgnJZOGaV8ES6QZSeS00bRGUOCSSlltai2zb/PqUN4VXALJgtCOvVl1yuFUL100PUXFQI7oyqQY0cZ61EKHI0U1pnJyW2zUsRyXVQZTqgQsW/q2LZtX4KCcBZcqqman86h66zDOMd1x3c3jliG6+Fybs0O1tGZr88+1o74UeVuYWd4zWHdnNqOQQHrN9wc4T52bIi6r9hndvBVPVEXlsf+cP9UOazDtZMFfzgGmVO6bV/fncUElQuWOLhhu8L2pALqd9aJqr/sjMfncPqZeMKMJ7w/uLOPwQP2AduKeuMc2xE1r3lO8ni6NZbZGTzW0VldO6xk2AsVOAzWoOa9+uzmzkobvN55LcQxFZCzvj0LWf9RZl4LSn9l/hn73c4Hj3KhA7DfVTzCtsS9bBu/d6BsCuplda7r36txiPOK/Ijj7+//fnWWnzLI/tQ7W5VtUfGK6xOOO+p67pvbKFBzSMkemzbq0W70W7C+iJHjHBN5am5jP7hPeM8wHkObld17ta7VPN1D+CnSTsHVzT5ipocqDPG09ZRMZliyQLUT2PI5XsSolJFB5QkUu39q8fLCCHCQoIIB59ijbA5qlxrHSQUWrg6sRxk6R7Lhf95VdYqOlSKPURxT994pB3VvnRLnMYs+MLGk+rkKVkKOvHLHUbljOQ4G+DgbzEz2brDO5bF9/sxjUDkf2dodDCqcZUM69XTsjJq/Su9xW2iD2KawQ6YCgziHfXXZTQzU4Xwc+8Bg3Vs5bkpWdFxRBkeo4T1xxHzUxbozPisnkW0GE0+d/kXbK8e5jHP8w3FHwin+sLwju6Jv/C6YP3/+fHrxa4ydy3jCcXBzwvVVEUzquhWCoYNqHQ/OQ2dcVdDLn/l7RR66a11AqORU887N16PI+hrflT+ufHwEE04819HWfHx8fMrowYxbZ0sccYXfMyhfnfvT2fBQdSIRomSLelQMEnCxGsZxoWf5/USsi93cUzJzvOLgssHiO8oSBIuyXy7LiW0P2hi2RU5mbJd/WCm+s61iv4rjl7BLKm5ym/YY3yPcPXH+hLtWxbxZooaKUXncuK0V3TPE0wIyh79bRjkUPJndrqpjv7EOftQuzrGSUscU2RXnUB6XGeWUZ0AFNEqZM1R7KnBC+VhRKGPBAYVS3AodA8NwzoMjndT3kNGlZ+6Rj8vGOCkjgApYsfxsFLBvrOzY2WCl7tZVJxDA69164zFVY1mtWyXn4Bg64/nMgdgZNiSObZsnYjt2RulgXIvxXekkftQuPgdBoGRydqdyelU/XRaLG6fKYc7WtLK7bF+xnugPE2RcTtlevB7Bjib3i4knbCdbLziuLts1zmP5sAUonyKXohwHAtg+HotxiWvCgUeSz2U8cfDCQBuj7K5aS531dbYNcOt15fpn1pG3QmeM1P3Nvnd0t6qD28L1ncm6GvCtgn0l9TnkQ1mUvEqvoj1AggDJpbA3f/78+UTYoA3CsYv/qDuY5Eb9rTJK3PeqP3id08V4rpqDbGPU3FOxAMdKHMPhnKlsMPdHbah3gTFE1h7qcY4r8PPLy8vfDY74jo91s41CmbEM/0ciT/kZMQY4JiquQDuj+sx+UGa/sHzmmzHU/UUiyqFb/x47NcTTIjpK/mgZp7xxsmVsPgb1akcgyuMxXgCqPpRDHa+MTZZplRlWdT3Wo8qjIXNGXu2ysxOcKfooo2Tlc/G9a3D2whmfqrwis5Qixe/uejyviKk47rKfts0HxSyb+5zVkY1Zt0zUPxis4iwbstpWFjy787x5gXo7dlHxpeL8bqdt25Zt0bbpzYGA0vFxXK3JFdJJjYOSB8E/BY42hF+2jnL++vV/v6qDNhcJHLTzSK5gn/DP7ehieYTSaZ3z2D8V0LGji8482wUMAjm4wuu4PfysfiFLyR7HeJNjxZ6osTpjnSoctS9X+xo/EbwOEDx3VJmsDjcHeQ3j2lK66kwiKpvrHDswKaDkwWCe9TWuTSaitm379EuqoROCfIrrWVZc8y7LJ9sgYDBhUcHFAC6WQd892sg23lF/unZDR27bvzHEa/GeKKi62R47HaNIJmdTlL+O53EM4jtn1PLmB16D/cBsMLQhivgJ24sbPnxv0AahfcFNerye+811c78ZK0kPCB4/F8s4P+osDPG0A2cGDpmB4rqckVHtqd2EaEc57HhdtkvLcnBdLjjPHPlsDKpxVDvfTjY8z0oOZWLjzoZUOQUO1XlmzqvrnJJ3CqQDDHpCJtWOOxbtuuwpJKZwHJnscuRXFTCtkEbVGBwtMxh0sNeGdEiSPe25eY3BAK/dAGbZql+tjDX9588fSz45GV3GLJ/P6uyQTsqRduPlbA3KyTaEiXkVROB3dIYzOTqkU4ZVnakc+s6OOQZrriwGQ/y9EwCz/eLAkgkr1ddMxx+xsffCM8n6THDz1/m/fE01p7v+YNbG2XABMccgTIrgOfbNcU26X7bENt2PWsTnqDu+h85R+hd1kdOzjviJdqox4XHJwDqc9aBLDuC/j49/jyUGmYLfsQ6uz0HZSax/Bejr8/zg9rD/OCZ8b3/9+pdVq+xSxCDxPz7jcX5vWLauOGaK67BvHH9in+NX89C/Uu/XUmNXcQZu7ajxvBdOJZ7O7MxZBjNTBp1rVtCV2QUTaqLzJIky6oWaHCDwQsAFycwttqUcZLXzHd/VDrdy5FXfFQGVZUYpcB3K2edAiRUY1+fILGdoWG4HVvBuR8AZgop0ckqlmvvYnstGis+hJDH1Wd0zvp9s/HlnAOcqXhdlFNHnjis5uJy6pwru+g7xOFjDLcezq+e7AfzROlQ9HSeo0x4HQrjeldPn1obKasQy6IixA4i72XHc6XZFUDjHfNu2L8GKe1+E6wsCxwX7E+1g25zJo+pB+fA+BLnE16Md5jFgHerkd2PF5My2bZ8cbpQP/YcMKhDCgCACoRgPzFB4f/+/dznFrwxhMKB+pYl1MdqGAO4eR5kIBDAgwLEIWdzcZzvjbILDkTXLa5WvUbJVPtMgJxJVoIl/iCxgdP6iui+8Cen8PyZWVV2sP51d6XxXsYiTMesv/8U6R10XJDH+6EJcz5lJLrMlSAjVnyjL8QyPZfxXJBj7tuEHc1udDNsspuG4LcpX9jNsCs4njI3++++/T3Yn/jATSsnn1gnOf5YHbWh8V7Ym9HL0y0H96ivOJyyHY4n/Wf74j/o/bFgQnWjLsB5nDzDDCfv6+vq6bdv2d87E/Ip4CutQ4Fgq7pvqUyfzK9oL+4x2mmXAeArHtIotFU7PeDrLAX8EdJ2FTp+dY8qkQVWHcyickYoFhYrnSAYUHt82vdPsdn8zdNqpxpH7ieW5z8ppzYIc3hVgRdeFUlDq/B5kjlRXrqxeRJatxLKoTCYux2XwWDXWK/3ee98Yz6THfio6+vs72atMzyqH0DmVWaCrAn3Up0guhdMTBIuzO1z/tuWZt5nMrAs6zn+F1TkQsrNMUZez3eH4bZt++WvWnwrcpnNCWWZlk1VQroBOKRN2WAad9E4/VPYS3nfc4cc+YJCBY642f+Kc6qdbK1fg6vp/MtgPxOP4/4p2nSxng33hThtKj7prV2WOujGIx9hDPaqNiOCc5VSZUuyDZrpUnUdCQ9krRWh0NgXUeFTXKFuiCKgoj6RTRgJh5hBnjl01/wNYf7W5oa7J4H6dr+sPhE3AR+gwtmQSBscYySs8H4/VY/0hK7azGse5MVGkE352RHdGfGVYueZbPmrXWcxntrFKLCllrj4rZ9oFCwF3jXL6AyozKCOS+Lwjw/DajgObKe2qHkc+cR2seJUzyrIxqeLqXyU+ziSdjoDbXQlucOzVbnycd8ouymzbZ4XonH4lr2qvkll9HgyeER1bxOW3zduf+I5l+drsOg5yWD9v278sls6mB9s0ZY86OuzoRoHqIxMWTJyp8UB9ie8oUdmgUW6PznIEERNLinSp6s3GEh1XzGQNYi3sQTjauMsaYGIpyvPmkSOMsK+8q4y7684PqgL1exJCOL5qfQ5R1UM2hpnvrXz5rP5t82QWr+sOqdlZqy6WyGRWfVSBNPe9siHb9tVnDh+PSZMAE1D4GX+5O+rma1kGznJkgsmRUKx7lV1Z2dxY8U2D+MDr1Pgqnxv7t23/HodncoF1a+hJliVDRhopO8Pj3amH61N9xH4x4jj2G+0A1s/xCM5TfGQx/jC7Gm0cxj7RDj5ih22GHVQbKdk4KLhMJwTaOc5euoX9OJ14uofRc6TPkeuzujoGqCqTOf14LDun4CYtk094DCcpOnyZQ43XR3nVf5SLy3GQVI0l1oNwqahsKNUYOFlYdrXTwfV0CBsmZ7CvlWPQxYpT3A3YFKmE7QU6j7ig0cDggdeEqt/1oRt4O8do8Bg40350nLxnDNIcQZSRRd2gJrsG62ZiBKGyRbFd1tFq51bZG+5z1pbrHx7r6msuz7aR68V+ofPqCBJ+hCSuR8ImjmXy8fXqGjVHOCjAYNPdf66H7wk66iqLIdrAR+yyzCe0GSrjSfUxZOHAgMcH3y2jAmSUWa23jh3BMmfpHOU7DPbB+QXKR890p9M51bxZiQfwWMfXydax6i/K6vQWgrPSlW5Afc/rGMcjfE31LicmLNQxR5yorHtFYrA9cvqzGl81Tu4YIxtvju/UXMJ3LuKfQ+heJPTcHFXI7D1eq7KHnK51/c/6gPKoMXQkC5MtcS3abraJWBe2H2XQlqHPpDKblNwqjlfrRI0Z3u8sew1lw/9nxaEZTiWeqqDvbNwrcOwEulUZd1MzBV6VVzJg+4p4YUczjjmChs+5AMEpxY6SqvrL4F1z7BfKgkoB23fBG35m46b60oHK+Ok4MWws3aMSXH6PbBVYhriW742SEY9hPegUoEOS7ch31hiPw710xuDxcGt7dQU6tojLr9aPUHYBSQtes6xvt+1zwMCbGEzUcCCgkNmXo3A73CiLIo5CrszeKfKNy4aDGeXwv5OP2+Fr1Bxhvax8D0dGKaD+5vdR8jtBwkGPnWJFEkVdeF7ZIWw7zsV3lUUVwQA/7oBjVa2xjg5Z9esyOBJhyKd1cNAZx7ZNEygrY6yCOYaaSxnpxLKpOrDtqm4s6+Z41Wc1fpkvxkSIsh2oN9A2cHC+bV+zo5BERn2DdWfgmEbJjP1WvnxWVt1fp1OUzlX+LF6v+slyYVZOXKN+3U/dJwcXCzr5sjGt2sjmfKYfOdPHZfkoX4aJKrVGFAka13L2Go8T3zcX9yCwrCOZsrWM4+GIuCtwl4ynq539FUPRlUVNNOVAdMpw+yyvCpZXnD5sL0uz27avSls5xnxcEVOVk43llCxIHHG71f1EeXCcnPx8nftVBqyrUvbqu6tzL1T/s0ykFazW4UgmVRcrx1DC/JL3KOPIJ2w3c27487OTCz8J3fVxBsHS1aePNn86tsg5dFiO63EOrsra4WOcKYKOLNuGKIfvfYpjSmdzoKE2TrjPXULmLKhdcqWjlG2rsovR8VW+gdJzKA/fGy6L4M0CNT+UDAG+j9E/rg/7iAEAf2eogECdx6yH+I42B+XAc9u2fSGgKjvTwUog30EVZA1qrBAqHR+c74kjCyq9G8ecHNl/1QbLvRKvZISX6jv3JxsvXItoQ7hdzH5CuxJl0M5EvRjX4NquyCdlJ7O1ns0FZ49cPdFflWkVZbJxRmA/USejfUb7ouqIcmou4rxz8wnB812NaTUu7jj7Da482pSwMdyuuu/u/UssO8ccTOqgHGr+IVHKcY8C9jP7ZUGUM5Mpjt3ChjztO55i8maGVzl+jE4ZV7YKft33kJ+/O6WC350iV23yYuHzWGfAsdWsxLCsqifOb9ux9ztV5BHLyOWyoCWU/IoDwsdXgtLugs7Kqbl0hHyqrkNZsqAEoRx3DECcQucghfvljHG1vrJ7NI76z8Xq+n0EsGOOxyvbhNevtMcZTXxs276mhqODt21fX26JAb8ibZS8Spd3dWXgrIwoZ/s6O+ohm3L8VX3bpn/cIoDH2MGvggosy44z1l3pfCULZhrFI3XYLjvBEQzw43dq7Hgucr34C0FILOE7nsL+vLy8/P0VLX4pebbe1JhnqIL8VVRB26CGI2M4sOXx7a4Dnj8ublHtZu115lxG/nD/bgHuG89/DobVtZy9uG1f3x/FGU8cYKPu7d7HkNfJxXB2TOkzFa/suRbLK/vL88FtFrkxUdlQyh7h+GJZbFvpPo5Vu3EZ3wP3K5NoKzi7FsGyxrWYceviap6/PFfxvqh3lcVx9d5DllHZ6y4465jH4mp78rTEU8Ap8atwFpmVlWEFUQUQq2OA5dVOpCNwkHziY1w/opN5lPVN1YP1sfJjZct9Q4XonFuum+VjxXnWPFxRINgmO/NnwPVNteUCG55favdA7V7z52xnRhk/1Zdb64rB4EqcETS4YADPq++8lqIefhwqyuLaRdJKERVM4jgZXWYsy4Ry7IUjwDLyKdM3GEih/Gir8M8FBC5bKj6rnW+EGxO0bx8fH60sIP4ccqu+YVl05uMYZj2x7VEbGlivy5xge4m73his4jxUQcMeEuAoycQyrJ4b7IfzR1frYPIJUZFOToYsNsA5lwX07M9W9R/xozLf2WVFBlms5FNPbKC+4rpCJ7KucJsF3Y0EB2WrVBshy7Z9zZ7Jrufjob9wbJVtjbJos3ls2OY6fyPGOiPQeE4pXcqPomH/MvItzmdxQJx3hIvqZ4dY4vNIbKLdZluu/BXckAtZ+H4iqpgrGy+WWY2Ja+MMPD3xdA9cQT6p8uw0uToQmSHicqp95UA7p7rTr6wMypHJntWlyDBXBzr7GCA5Q9slpKp7tNKfVYN+FZlSOT/blpNdaNg5UECFisGD2kVwj9yptrJxcPdonPXBM+MK8mnbPLFQBUfsuOB5RdTEDp/rU5YNhVBZL2qXd89YVYSWk7tjJ0M2FUCEo4oBCbfDsjAJowguRwB1xkZlvTon3PUt6uGgIsoi6RTnw0H+9etX+a4MtYmB73CKdjg4i89H7IyD8nPOsj1jw26HTP/FeaUvOvdoleQ5Q5fh8aw+DJyx7crn6hzDOnDNcxDPOh6DdNSRvJa37TOxzD4o99HJq2IWLuM+u/5yvFGRXFW8p8Yo/leyKRvJ9sLNgcq+dmyN+xEIRY4wFFmFiGMZyaJkVbbGgeeeOs42DfH6+vr3syOcsqdb1BpQMuJnludWtuQuxNNeJcx1dOqpFmpHeZ/h2HfaiHKsNLZNv2hMycXvzVFKGBGTG6/Bul1GFO8qsjxK8WF9DJddVBFTXCc/Sof1xPkwWKq+TvqtS+V09bGszrgopeWw4ryysqmUeHa9glKGTBjxXME5xCTUnz9/PgUbeL/QEGUGzxmUFeW6ooTPGMdBD931cUVgd9QWrLR1pMxZNitbVzjGHNDHulYBfaz9+HNt4fWsr5GYwWPbpnUs658uqmxdfB9Ddq2zRWx7FXHEuhV1Xow1j1M8aob2W5Ex0WblqKJjik583Gcug+WCLGJ/Ro0l62f0iaK//KtNPPbON3l9ff30PYKKIKVQNpynTl5sj+VVfcMxwvKOqOC/7Nwtg4afCtZ1AUemOJ2T1aOyRnjtMmGrynPbWbDOMoRc3G41zzrEhmub7TXrLRyX0HG8YRFl1a+zKagYQBEtrOO5/yqbCMuhj8vAuv78+SPvNetUJji4r8oWqXuG8oQOZLvK9k/ZkegfEkdxbdwLHKM4pvwHtAEsM96vGAtcC2grnE7lcVV9VOOGsiv9jbFOlOdxDrkw1sF79+fPn+319fVLFt6vX78+kVIsA6Pj+7GNVtdm653lX7U9PzbjqXtz9pABe9A1ZpWDyE6pUvYOSCqxDFyne0QgoB6TyMbHETKqXxmwHnWtawcVrXpMYnW+oDxKxgA7rXvnUCelslPuDKDCjDaVgVc70+rzCrrzhNtZUZyrSnavcv6JWL1vDkfWUhdn1b8i69lzSK055VBktkAFQOywYDl2HtHeYNuVrmZcrdvYtrBzqTKsUF5lQ5kkcdlBAWeX+D6iblX3UH1nu6f0tmpTESCc7t+1n1Ee6+V2MXBRwTHXGY47k0vcF/7BCzVG2fEVf8udq9b32JDbYyWQW70/q75Kt/4V28R6PLMDnbZW/bgsxkJdguQNv8eJ/XYkMjjTkTcoXFyD/XaxQ5ArzgYieIM1Gy9FbuD8cu0pwijazvQTkj5oj1G/qrglyBbU+yp+xLKsu1EuRdCxTeV1xuOiMoWytanIXvXZzdMYsyDCwj5xm0iY4nH1wxY87mibnM44ajuUzNW632uPfizxlIEV7pUBjGvLObDqc3zHOlxQgHBBAytYd2zb/C8NxTkmn7gtxGo2lKuLFSSPU8ZwOwOzB515k8la1bGqaNQO/FmOrBpHPocBSChW/gUH/Ix1OaKK59ZVjrmqd4KAwRHc0s7skWfbPGmakQm8FpXuV9kkrty2fX2XX+bg4nVnw8nDbbosmMwWuQCusovKF+BgDXdbObCIcuxYox5mvfvr169PWTzYP2VnXJawg9OtHLyEnAqcGYK7xmF7YqcegwJ+JAcDi04g7s6rYEh9x/8q2By7cztkxDfPBb5v6prsM2OFyMyu2wM11zoBaRavqLL8mfvrskbxhwK2Td8nJJUw4wYJpyyucZn6jD2+oYvNkDTjYyyDim3U9XwNyhdjgD42jg/fF/bbse24DsdO3X/OsHX2G8cISRfuE+tIRzLxWLl7wMSYk1HJjHM2so5VXWpev7+/f4mH0EdSGyPq/mf9Vt8VlN3h64/YoSGetnxXF8+fDUc4dYgmrMNNvlXjgOcd+bRt/lFAR+LgNRnc4s6IItUHRUIpJZ3VvRq4ZFlWDplxcAFgILvH1f3H83v76YAKFo99fHzI56V554oVr1K2qu4rkTlhEwTcB8847re2MyvOb0aAKXKj0y7+R/2oHolTziyeUxm22QbBWSSe0+GKMFJ2x0HZ0NCDXC9/rtpAW8dBANfJRA7PAw7c8Dr2WdCesPPfmTsq2OLzTDhxEIPgF6Jv2/+RT+z4Y9YCb5axXOp75kt1SKOOXcExzgiOwbmoCJNt69+HbD07f899V2sjjnd1dWWHuJ7Kn8ziCtVf98f1RkDP5VAnxWcmmNBOYB/52m37/JP0ri7uD+qX1c1qp0f4uyKd8LN7PEy15TJqWbfzK1vwHNaXETM49liXutcKOP640YF9dHrR9RX7rOSNtrBMJXP0E0m7X79+bf/999/258+fT3Lz46HYBxdD4niotrN1xu24elAOdY2zPXtt0BBPAHbMKuLgzPbU54o44v+qDH/eNr1bzYoddzszAso52+xYZ06ykpH7otpQ9bhrXWYRKk4ukylVxl65uvUfnYu8+3sWXD9USjKWC0X7/v7+5Seu8YWRvIvisqJuhXH0B0fRIXvOaKOqs7I/+NnZGWUzt+3zYwXOiWcbo4KJLPOWv2O51cCrGqfsGnTGMpKCAx9FcLiMqsy+czu4c4p1IznE70HJCJxt+/prpKpeHIcIFleg5pEKcDAQ4J15Bjr0OD7xnzOe4pqXl3+PRCAhpbLz8Dpu28mUHWenXjn4HUJgcA46ejTTtypoXdHN/D0jL4/akSNzKSOdnK5SZAbWpQgn9wQE2yo+xroL/cwgWzBLKo5t29fHe1UbbiMk2yAJMOmQkQRurvG13TnGvjXejxgD9u25TDzKHPpWvftQ6e0M2DaOO5J9Sj924gLlW/Afyoz/FTBb7OPj36/n/ffff3/lVO3GPQg7FLK4DXeVHJHpBWyHs6zUGKp6KluE13T1x48nnqog4AqjXhmUTHFzEKCCAueoqv44JeYCADQE3QyoTMFwSiuDlXxVj+ojX8uKCQMA7DOfWwHXw1DBkuqLkp+RKVqV0nlkTrv7yoraKUw2EGzw8SetGdweP/5wtG8ZlKN/xngO1tEd76NO+NlYDSYcuv2q6uR6VmwREybcL2cz2LFjW8IOrvpckU9Xzg9lC1zWLI6dCqQ6GzUVAeXAQQTKlNlUvC9Vu6F/MRjA/6t6MfMBVLAaO8vO5qBc8bPrmG0XL3MNoO1RgVhgxeGv+qr+Z45+FSQMrocLuhC4juJ/N0DsxiKOYOjMiYy4wPYdYcTl8Fj3uyJwVB+wPL+njbPoHVSMhPWgXti2f3pXkVJKJ2BdK8ejLVVO9UnFNo7M6NhwrgPtU/Qfr0MZVdwU9apfqGO5ue+MkIkf03a60m2uV/dKEVBxHO832xqeQ3ENyhE2JuYp23PUD1EmNo6YfAoZ4vrO5juPzQo6tojLd/HjiSeFPUTDmfV32nfkE5/D8sgydxW1IlBY4bAjxoSV6o9atAocvChkJE+HOHIBAPat2rFQZBbLh9/dTj3Xh/J3AlVUCmzE8JqzsoU4UKwMs5qj8T+e24/HIfBdG3Etk023yHzqOACDQQdX25YVuMCFy6jgh0kVXPdx3AXuAcx84mNsV1RmFJNPq+jcB+XEZuPm7IA6zvY1c/IzqAxe3nHGYAPJIUc+dWwNOthINsUfBwx7bCgTTuj8xy4xZiMglNP933///S0b8jH5hAGAc/5VBq8L2Pk7HlfyZte5uga3wV7SCf9XdfI5pX95nqzalKuucWVcTFL1TdUTf6yLw3awHlHHqqc4OCsSCWmWV8VhrDudLkVbxhko2Vjg2HH/1Hf8zPWzvUVwZiz2jfViADeU4xequc/bpvU7byRzn5V+RPuD7WM5NQ4or8vAiv6irUEbFPXj5obqQ8iJtlq9YxBjZpVly7K7LNwVOFIpszWdtZphiKf/H0oJ3op8cgp423o7j3GOr+XzHSWWgZ1htbvLTliV0YT1Kqid3z31oFJCuDR8duZXAxtHgrm2AqtzzqXiZt+rXRWF7vxARa2Ub7SvgktWoki6KYJpr6LdiyGgBmcg0/2PJo+yPdW6q+xMh2xSzj46pmfY6s7aVRsuIQs7X8qx5V1YRzRxW9EeQo07EijskCoHNdvIqHwIhHM8lQMbsnV2Z7EfSEzGMXb+8buSXxFgsaOM9Uf/+ReGcOc52+RQ96ZrL3isVICVHR9cj2y+87rnAD2Oq/Jq3e2NRZQe2oNODKH0X1ZGyeeC22xeK1IBdQOu09A5Hx8fnx4V5s1otYGB+kRls2RExWqfsF0kUvg6RSCpc+qHHDLSH+cqz1uuQ811RJArQcTgHOFjSnaX4YRjyvELZ9iqOIHbcYQXj2nYh7A1//M///OXjIt5FDKEXYkxQD2g9AT3TxFW/N1tZHXhxigrm9kjdb6DIZ4AZynulfaqtrpBSia7mhSOFODzbETwOmSK2Wiyw8mKzwU0DlnmkWqTr8vq5V1hJ3OGTJmqPnTvY+eaFfmuyg7iHXyl7EMOVJ5IKqGSdyy/IqgUrnLKx9kfnIFHIJwQlTwZMZEFTi4oqcgpJqPUSzkzHX3F2GJ7arw6Ng2v451WHpM9uhrHA4myiiha8THYCVWZtRwMrOpNJ0OQQ/GfM57UXHT9VrYH7W32S3wrO80rQeiVdQzWkQVc+LkinPB759511mG3/BFk/r8rr3SJkxn1j1urHKS7py+QJI7reFMXy8evjnEM4I65vqp+Zv1WcHFbZmsd+ZQRX9n9U8QS18HHkPTH+8mEk5KXkcVMTMRxOyijI94y0kzFsFiONzowE4zfQ8Uk4sfHx/b6+vqpDpaZ53U2Rtm97OgApb86qOz5Sl27iCc12c/GLdo4KkM10CtyOwed63NtOoWkDCOWj8WQybNtn99rgPWoxcIKf9u+svDY/grc7q5y+lfqVPeKH7s7gmxXJXsMgw1hpqBQ0WHqqSKd2DBkcnPfIxCM+6yMrnPEWLnjLzkxuRhlcbce+4UyZkFA5ggpQ+aMWmb0Bz8XR5zLPThSjyNLtu3ru2ti7fK65nrUMQ4olL5G2xPfsa0IJFhPRB2sS7kfXJblVbakQifDVMHZF9zl7Mrh3msS48R9RRKPxwllYIc7Cxyz4EaV4XFzAVN8xvuOjv+2/fspdTz+8vIigyB8VCZe9BptxS8OqUCF7Rbabww00H46O6NshbI1isDjz9V9GeyD0lMB9qnQr0HdwnoTj2FdOB/40S3no7Gs/JnnCmeGuv+qr66trJw7p/qJ53hMuV0eU/SHcX3jegxdEOUC8SiU2sRAmTKfG3WQGh/1FIjqjxovpwsQSHgoveCQ2e347J7ScPN92z7/EmvIjT8WFOMd48VxB45XPPLs9CK2G3Wj/Bkx4l6QzpltKmYJsujPnz+fxgPtDvY3+oEbGjgmr6+vf/sVGVLRTvTl9fX175/rM8qnNnrQ9mY2hm05f8ZruZ29sXGbeOpM7FsYwbMNbnfQjra7cnMyA1gZgsqAchk34Zz8mWFipb1t/iXaURbLZf3LcKQO1eeqD1ldTrGzjAjOFlJ1YR1VP5Tzsod0iuvYmGbA4EYpcVVWvQtEBSb4nwMmVTfPWeUIqGtdfdXcd9cOBtvWswFnlcmuUXM/WweZXlJrM7MzfEw5OVg3Xoe6iAOITI+5rNasnxW6dlOB7UugY2eC+GBZ2FFW18QY8i6yuhdRL/6PzxgwqjK4OeAIJxXMobzqnqj7g0EAklDoMIc9iQAh2mTiD8ePgxp1nmXLfAl13tkMFQyoOvG+Vz7f4CucXlNBOq8BRZQwSdJtO77z/excz/V05k3Vv0zOs5Dp+UoGp7+5PtUnfHwW0X3CwfmPTqdv29f3tLKcLHPlkzt94dpHOdy8YXB/kMjjuc7vYkUSBokpfDSN5eXH1j4+Pv6SMirbKf6r7CJnl1Bu3HTBscmuRRuDtoaBP2SBtqfyM6IN1C/V3K/AY4f1dG0+ls+wwgHNo3YLWHU2O45Bt90siI7vWDZzgrCMUn74uQresYxLWUW4AAB3FzpQY7lKFCEyw9s1Sp1yPB5R/7blv3SnFBBDBXLK8a+CAtdmlFHBmyvvHCw8x4bMrZVQzNkjEFfBjffK98HgXjjDFqnrunYmjle2h21atkOdOXIqIOAAoAoisjbxcydFvvNIYKeebds+ObFsO9CeRF+j7/GjDVGH2lBghxcdVEU2dbJ04jv+x+squ811qd1qljs+h4x4XgUBbkMoA/fPOeeZHVDB1Mr1g2uhdBaf75JO7h6vBGx8naoj019d4uFsdNZAJZOLfRhIfPMY4eN4/OJsp0txswMJCgfuh9Jvqo7qu2s38/H5OOo71m+ZLQ/wY81oGzDjNDJ7+GkFzJqJOvBc1MWfOdsn071ol1SMHP13fUWZ8TiPnSKemDRymxR43sXWTtfshSLp4rv6n6HiGCoM8dSEI50ywiLOs2NbQRkRNnoZAVUpLPzulJSry5XJyCeEU8J4roOMlFmpR4HTn3kHNEO1i433jdl23IEOI9kxdFi3S6uM8+o4nmOw8cV7hedWlKS6d0pJs6HgHfpbIwukXDlXZjDYi9X5dMQWYVm2P+q7szOKbKrWjqoTdcDKRgPqKiQ/2PFzcjkZ99qFkHu17bALLAvWz7umcT4eLVPtx+5y6GElO+5io53BACHqQVuk5FQ+ispoUw5ylEfnHW1QEJX4SByWYbJAjXEXmf/E5bLveKxrZwa3gSKduoGhuu+dgM35/VyW9YYrW81pR6xlWAk8XUzi2u3aE9UO9pnrDf2C2Y9xPMorv5x99IpEUuPB7fH1jljBY87esE1y9oZlUnIq8MYNk/7cH854irH9/fv39vr6+onAijoV8cS2hLNpsczHx8cX2xTl1DhEbIePTYYcimxCO8LEnbJHHBdlY+xIp6gHH1dU9yZDRdLxH1+TybwXQzwtokM6xbnMeFRtuGDBKWiWpwoKogxPtiy46BJRFfm0bXqndgXKAO1dCHz9UeKqqgsVH48XyoPXdzOpnMFyRJT6zGWUwcVx56wEdx8wqGCHowoEHt3xdgr70eV+RhwxeD8RR2yRqmvbfPZSZmfYTqHeY6IEz7PucYQ9Qz2yy2WVDJ3+d49zGazfvVswA9uJLOMp3iuxbdtfhz+Ajn58j3qyjQneNd22rwEABw5Rhq9TY6OyClTQgf1QAVAEOVy/2unPsmizTacsEMQy2bkucTW4LzLyB8E6sEsidogLXqsuqGW/ist0CFduq+vXqJihumbPObY5rAe4XdwofXn5/O44RTZtm//1ZbYj3Y0PRHZ9pRPUPVExQ2b7nN1U9xnHNbKaOGaJd+7FuAbhFP9jE4HPI1nDep43MnD9cNmom+2QQtinsA/dJ21wnuFfEFgqnmFSLpsrSgZFaClbWsmfZcXxOHIZxSs4H6+LIZ5OxFGnfm8binjJSCRlHB2hFROvkyaKi44VE0IFF+68w6rid+gY/D1ElAtmlHPNx9AIMvFT9QUVLv/ncoiMIAu5oxynHnM/3Zhmc4Ll4+9Z8HFLTKAwuDe68+8eBJ2zM4r4woDA6Q6lF2O3VAULARccqDHJdqJZJqUnO7o523XFtqp7xvqX689sBmaUhuOvxkUFbuyoKpIpPuMf11GNFWcisNxqN9yRUdU6YVKMj0ddTD4pG6TGRpXp2o+xM48DtzYz8kn9V8dYP1ZyMMGSyds9nrWF37tB7p72OmB7wrYEj6Ne4+Afy7mNXSQjMOBX/mtn48ORcainWYdg3Wpj2tklp5tc3MUERHZtPKLIGaTxGDeOU+jJ+AEHJlHiPsTnIP8VqYREVPw524P9UOstEDJFebV5jvdhrz52tkORPIoAClkw66pDGiqwHVY2SdWjbOwRDPHUhFvM+Llysleh6lF1KSeLj2VlUGlzH5Vid2W4blSayjFW5bDfXRwhoRzhsufedcgqDJpUemq0rwInvIfqPrDxUY5/JhvPF2X0FEnGj8jFMWfwsC1nbDnQYfluiUoRuyCjqmOwH5WOmPHWJFB8Xqlj23yAtMcWqeNRt3JOo2z88W6p0mfhRCqdlAVubKf4uMLKpgA68Nx+13Y55zXGxtkGfEEsBlX4K3kx7q6OCBDQ0ceAgQMAtjlKV3IwwI/b4aMQGICE7C4gZ9uCfeBxUJ9XN5zcuuo48mpcuJyz/YPrsWpvnM+igjxHSmTtMjHE7e6dI679rj+TXe/OVQReFvuo8avIPLcJgHWo2MX5xArKf1f9UjHWns2Nrv+J9pDrVu0rAuTPnz9fyKdt+/y4Nv/ypyL20M7HeIUMUVfYGvzuiKc4hrKrOALXDt5bLqtiFf4VP/RBcPxQZpSF5XJkWWd9sT3jck73qHGp5OxgRecM8bQDyhng85nCXWlnhXzCcm4yuj5UAUOnjGpn274qOfUIBJZbHa8O4eOgjNtqHQG1q4IIhRxlsYx6VAJfflgZS2VIQo74r3YzMseCgzbsRxBNak7heQaTnSyfU84hx4oiPBudAEIdu5e8g8EVtii+b5sOTCo7lemarl1SOknVyTrJpdVnNsthZV2z/lXE/J5H71ygyWODzvKfP3/+/qRzyBHyYUDAAREGBegwb9v2xfHmPmd2iOWOMkE6hfzqfSE4ZkxMslPPsmLf1PHVe5Cdc459t76xIY8L5yOzr+L8creGFaoyR0jJWxKajjhT6NgQLItkAJeN84pMYmIJr0V7w6RSlM/66ja4Uabu3Kj8S6dfXcyAelvpcOwHP7LNc3/bti/vTFL9Qz0e312GE5I4imjC7/gLe7wO+d4yKRb3Ce81txl2KOpE0sxtuPD4cj/5z9mnDCs2iO999j9Dtg4rDPF0ANXNPkORd+tBBeWuccEBtpPVg2W4rJNFgRd2VraDM8b6DDlcYBH1qoWvspv+/PnzSSYlV5W1xMpLkU78Wcnbubdh4Dmwq5QSK2HunwoE0ADcA6uKdgKGwb1xli3q1texM1iOdU1Hf6C+yZx2Jt2c7lgdo9V1zfq32g3PEP11wU+QL7w7//7++QXjqs+YGaXuBzrYEYCwY49l1fUZsP14rCPaw51llCPuaZRRQQzK6Y6hrC5QxHdBZX1RNs3N0SzgHDwGnG7A+6dIJ3eO61ZrEb9Xn1Wde3EFAeX6tkJABTK/OoC+qPMZ8RjqHWzH+cCZP86bCs62oH5Weod99GwOKV3kyrDNRD+cf7iCf4xi2z4/Do0Zqbxpgb+iihsIcR2WVeQLZg7xj1aoDKP4Q/mZyFGy4CaFIodwjNSGOm6WsH1i26jiGZYX289szJ51qmxdx04dbZfRJp46QeQtcLQdHuyO4ruHE3BWP1XfnNLkXU5UmqqMaot3S9UixTbVO4KyF326PpxFCLigaXUxVg42P6qG9YRyyx7HULKxcuNxZCXX6RfWhW3yT9LiPMnmC84vbFvtkiO4L2ongfundm3YIeRdH3YcsQyPiRrPrMwVDt1Pxb0DM57TneBxZQOh2/YqOvVnupEDBmVnurpZ2RrUG9gO747GOZQJ9SgHAsqWIdTjFkr2jJjgMt05WtmKqh1VDz5WF+B3Q7y8vHwKHD4+Pj4ROFEmzqG+xUAAr8fP+J8DGp43eH/w/VPoxEcf4l5hYKM2QkKekPf19XV7fX39Eqgo24FycjCixp7hdII73kUm6+B8OH/W+QqujrPAa6dDiMX3TB6ny/l6Vafz9bl+VzbzldS4s1x8f4JUcD45nmPyJ65lW4EkRTVWmX1R94zbwDHo+KN8XtWp7B+/hynqwf8sJ9rp0NVRPjYiQt/+z//8z5e6Pj4+/j6mx/dJ3VvWuUhCxeYH2iQcm8pGcx/w8cAYM/QtsO/4qCG2E/K9vr5+eiwQHw9UcUvUgbY1juFjhupeqb4pu1XZOXWvEBnxmcmjsCvj6RYB1K2CNHRcV3HVdd0b6AL6I7K5yeaUg2ofjQEHK65NLquwN9Mlc9C3zafJriwklr0aJ5YnC5rwOqdMUSG4XRP+7upy77hS9xV3pUPOjDjEOpnV52AgjjmliNeiYs2UYqV8q3vecfRv5YQOclypZ/Fzd+2fhUeZQ87W4HGn01WQkemq+J4F9M6BV3ZDOf+O0FKyOLB+qEglZW9XyrtzqM/e39//ZtEikHQKPcvBAG4MYX3hIOM51uNOJpaZHVomB9G5x3aRFAibxQEn7pgz8cTvCmEiCm2UetQD+7dqP7rH99Q1uAZdkqcq3wXrUW7LkWJ4rZqXLBcTKc7fV/JwnRy0OwJJ+ZYsYzbeTnZuW9loZQ8wg4eJmdBHysYo+9bx8RUq3z4jnNy1GanFui2rJ5Nr2/6RNq+vr383Pf7777+/53/9+vWJJEI9HSQO6nVsA/Uy6mE8hjbHbRTz55A72lTzSMVf0W7YTx4HtDVIOqHtUfGKsrFxTpFO3E8kqrA+NQZOPzifLuD8r1VbNI/afRMoR5knCSvhjrHkss5IZcYAy/DEDiXkZOruDnRQKecjcO8OQfCug3pmnNN0HUnGhvso2YRQY4zyhtJDEgqv4xfCurZRwbKidUrT9ZGPq+udATpDkQ4GDKdjvyMqO+NsRMcmqXqz8ypjJ7MzVRlur2svqh1X/tydLx07g23HezkiqHK/HPf6+moznrBPrLdRLiRnmKBxAZSy5Sr4iGtxk4ODBLe5wbvOjnRCO4T9UOOvggfua2bDBveFuzfKX1XXuvuPQD2XrW+cx24usdzdvrHf7fqLn93YZJsJWCYjxVSMUcmY9TPb2ODxy8gnjltQ14RPq16NUcUiSGAw+AkQ7oMiGHh+7PHpWRd36nEbOmhT+D4rQgrjB6xHkR5oa7Zt+0Q8Kf2N13d8fCS88D2C8Z0z3VSchWPJtibIJrQ78R/vL8c+buMm/vPnTmxbralqvDrXdTHE03Z7p+Cs9nihshJW5+O7Myjuu4MLLFwZJRs7/FkfA50dBC5/JTrycCDG1+AuBPaX393hxkg59pwKurdv8Z9l4R0iPM6ZT5ym7HZcWBGrQIb7r5SxMzbKqXIK/mxMAHI73GusHVF/Frp6+VZwfa3sTNdBXBnDzH44O9O1Rdt2jh3JgtnqvnXGAR3pADrQrt14bBrf1aHKsp5m4HHUt5lzjHZKBWNhZ1TfXl5etv/93/+17w2J63gH+r///tve39/l43fcP0cAOLujbFB1/iyMjVmHIie27R+Jzesm5lUnmOO1xOvKkbydOYL1o/5w88/1WbXPcHK6so54wvZVkM1+Mrft6uK+x2dHMvH126ZfIh4+rdKnTEbEuc4YcX1qHFQ/WV4+p/oXUCRWNpedDYz7wD58tgEfZfleIMHD95vjA0U24fm4xhGs/Pnj41/mkrItOGbv7/+XORzXIJGGZTnjif9jH9Sv8+G9dRshOO4sQ3x3dkkdy+baCrrX3IV4uqVT/NPgnFdWzNk9UIGB2xFBqEDC1c3XsdPpwE7BEXA/j6Ijj1Nq6hyXcUFD1rYjajLgToSrLwwPvo8DjTbuDgWYNEOjolJI2fFnw+x2BZwSdcHDrdAJLAfPB6X3FMFySzzCXKtsjrIzqg61k8p1IDL7VNmZrEyl31ftHjusmVxdGaqyGIhFhhMHeqi3XTCMuhfHm8/hd+5zgIM3Hgu0M/G4htqIwnd04JyKc+j8c/AS9UZ5RTpxIKDsjnPmO47+4PZw98ERGUhoVnUwMn2ckU74x3Pb1enky2TNdIvSv4pAc8QQlneBMLbv6unELfi5Q0IEkPDmz6ijWF/xjzEoXZ5taLD947Hgc9gGnu/MQy6TEesoGwOJuPf3z1m0TJLgPVD95/c9Kfse9fE7ndRc6qxRJpnwHuOv9rEMcR///Plj4zF+rBszn3iTQ61xRThxfIQxE88FZbv26KsMKkZcqXcynr4BeDGr7w4dsgiZ/0qGFUJLyeGMH6eI7nnUDq/H72c5gZ1Aj4kmHBMkZtwz2XGuE4BkykZd3x1TDASUXCotVbWhFCgrY1bC0RelWDMl23H4JyAYHAHrUkeAfFfcys5gfV05Ou/Kc2VWskZdYKDkyoj9qv4KipgJ4C/FYZZTICOeUIbQw5yh5PQ0H4u2VN9cJgEGCvEOkbAhfP+wXiSfcLc5ggK0Nxw48UYInud+V0G1Oz+4H5RP6Ejy+MxzVdWDqHzj7LyaK9lmxy03HDrEV3znmCTz21R9yp66PqvNChxjriv0Desyfn8plom6mMSIdpiA4k1bJKPcuKljXdIJj2VEliIP1NgxeRbxSujfyBwKe8IEzq9fvz79ciC2xedUnBDEU6ZL3Xi4uRXzJP7jUxqs7+MXYfGRPJ5/SDyxzeFjOP787ipFTuE12Xu51IZJZ425cXJrrjO+Djcnnu69C6vQGbSz5N7rOLp63O5HNllwsTjDpYInZ5Az0ouPxeSv6uFx4MVVGXKu231f2UGusEqGubb//PkjnW4cixUF0u2jCwhU3c5Qx3EkoBCo2JWDjwqWX+jqHH38rIx45uhXivbsoGCCjNvgLD37iLilvepA2RD8vGpnok6ngzJ7xGVQZ2aP0CnHGo93+p6VYd11pK4OsI+cSaAeG0EHtiKeWB6ljzmYcT84kY2HehTl5eXlb6aWylBg+bF9dvDdC1/dzr1y4J0N6pS5CmNjeuj6UIq04POoz1YID6zPoUNsrfQra0ddi33I9ALLyTpfxSfZemAdHsc68QZ+5u+ZPWJiCfUZ+7rZZgZm73CdQdIo25KRa1g363TVb9bBfCw7h5vH2WOFWJ6/Y7JA1KNiApxbLuuJfwTD+fNMHqm+4n8sH3Yw/mKc397e/pJOLy8vf7OdMv+EbUtk2v7333+fbImyPUw64QvM+Xgc4/azPx6zav0pZFxDBw/5qN0Yzj5Q8SqljOgYMBcs4LmsXJe8qpSrak/1vQsueybhtFIn7/CqfsVOAZNNZ0AZ4YALVhScEcbz3UDKOfcsL7anFCdf45SsqndV8Q4GDkpvfee5dLadqc53xlYFgmqHmW0nl8l8lRUd0iGcuvqyC+4n6moOqEJ/87v5sP/dRw+xP26nHeHuI14b2VlBmmHdTDyFrFhvOP/osPOv3KGMGAREG8rOuO/qOPfRfR7cDi54DX3AQXCXOFq91tVR6aHseKV/FWGj/HnuD8vMMYcbU+VruqB3FUoXq/fGqWvYD2Y9GYRIIDJz8Ad0FAmlCJvQtfiKigDOO0cuqb5xGda7qM+439hfREa0uXFi4oqfUnl5efn7C3YI3uhQxBP+VRlarH9dH7lPbEfimvhF2DjvSDS895xd+99//306hrGOsjNsb1QGFPafbaUbB7zmrLXH9XYwj9p9AzgCQ5ULuPLKYVfnog4ux9c7xaCcTbdz4QK5Tp+5jW27/kXjGVg5Z4tVZSCtBLKs+APs8O4ZU2dYV4kyZRRZKSuDg2VX2onPrsxgsAeOgP+JOMPOIKpNh04bUY6dS2XLFJGRtY9YtS1XzRGlK8Ohd5mtarc42+12dsUFBlWQr8YispxCNg4GOftJ9Svq4T98XwjaBwwCuD8hv7M9fFx9zuzx4DGhCJeA0mFc3m3wddrN2siuqdrpbA5m/cjAa4QJBV7zq2ugIuSwDS6f2Rw+ztmhSkfGEwrZo75BFmVyIRT5hOcc+eTQyUrlNqpsJ65HbZTHJjrWheOHRA7qekUAZboU5yg/4ueuVX1CmUMWTAQIuSLryRFPvNGBP2jBhJKaH9j3ilxS95HXXzdWWokBj2CIpybcTasU3556Mkd6VT4875y8zs4Hy6ocSlXGnXfkk6on61NWfo9Dd8QJXF2w3bTVrOweZORXJ6DjXZ24l/zycBe8qZ0ZRUCp55hZCaOCVcoWy/Cxzv8Kq0HGIMcjjlem37rolO/Wt5dw34uqvapvzpmp7IwDktzqT8nMxNK29R6N7uhDJ/+Ks9c9fvSe4r1Qjrd7T4kqE3ABidONeA0/jqHa4eDPyaAIRbQXYVP4u7Ix6ruzM26HuWt3eGz22qLATya9r4IjCPhYRkDFd7euu36YOpeRQmrj1vUlg/P50Z9zfljonUo/8zFuvyLVcE1yX/la7kMcx8fMsF4mWNAPZrviSKHoA/8atOpDnKsIH3Wdg/Kl1bizzea5qTYv8BrsExJBSOLgNYqMcu0puDFUY8V9w/sZc5U3NOJ7PCIZ39WcCnmRgIpMp5BFbajzf64Lr+VxyWyFi4tQblVHtR7jmKs7w0MSTx2FeEvD6gKPPdd3buZq/a4tV48rgwsyq4cDhiqoqIxERYSp484hztrDcqqePfcJ0ZmTnXqUA56Ntau7Q1atElo4xlngsm39oI0VKL5EL845hwbLKSee22K51Ge33ivlrj4Pvic6euYoCX4EHfmO1r9tenc9swfqsyu3V67QS5n9qY4FkaF+YhmdVES2g8yPLXT6UX1eQUZGIJHnfuACx0b9OinLtroTn51ju67sjiPJUDZ29tW7QFQwoILo7i6zgrNTXVvUwZVr/6chuweKQNlbN88LtYlXzQfWqxlRw8F2RopFeeeHsg/G/cDjylacOV9VlkhFAKrsFfRxOfMHfXLsExJK3K84jo8MY8aPA57r6CzGy8vX7Cn0s7ktvkc8ltgmnse2sNz7+7/HpB15p7KlWHerMeE4BN/TxHVhPep+YnnuP9aJjxYqwguJJfzP76zFOIf7qewVn2e7w/eC5eKy1Xp3yAioLh6SeBpcA6VQ2JiwYVEGiR36I2XcOSVPFZSwQYhj3bFRn7Njq+gsTtU/Vqqda64CKlt1P5wRdQ6LKsOf47tSsJUTf8Z9c/WcVffgOVHpo+8I55R2r902b2fwu2ovC44CjtDCduO/0qvZ5sbq/d6rHypbVF3nbDF/Vn3OMli5jMOZ2bmq7lUSb9v0BgfXG8erDCZlWzr3aWzIc2LP5vBeu5ARWZUcTK5UyHwp1g3cxlmbKh2d7uQOeaprXRuozzF2UI98MWGCuuf3799fbENFJu0F62rcYFmFkk/58wxH4uA1TOjgBg6PX5UwwGSJkivqRXIH5VGEDcoesjKxqK4LIk3FuEg8YdshFz5+x+PI/UXCKY4fxV7fIspncfqK7rkL8TTGtsaZRIcimzrts8OfneuW6bahruvIvccxvdV87Ix9ViYjdbJgrsKqk4RK2pFP29Z/lEUx+3yOr6nq3HNuBWcTW4PHh1onexznZ4PSK2cRbpXDWW0+dAisjITZtq8bFo78V1kH6IyegbPIqsoW4zWhe3l8mWByTv+ZUO3xcfzJblVW1akebYhzbG/4OJdV57J+HCkzeAwowmXbPMGN/105d133e/ecg5NJrUHsv7ODam1UGwt7ZO7EGZXNwjWP18R3fowqjquMHaVTO/YICa4uUcWZS533PFVzr0NuKh0Z4O+Y0YVyRlmcQ87uukcMuUxmkzimwAwjtPkvL/8efcT7ETKp70oGbAvtDb9UHF86jnMVs7XYXmF5dx9Cvize5s9de5Zhj194c+LpTKf1u+KsMeqSQtuW75LyMRUMdK7D9pQsWR865fc4xp3F+Qjo9C17VvxsWarMJ97VyMABARpmdmj4/rg/rCe7xyt4pPkwuB2OkMVY5qdDOUTZdz6XjTM7tBx8xLUucFJkuSLUV9+/x0FChj22qKvb1Ni5cQ64fnUJn1U434CDPZSva2eUQx91xHnchcZrMhuDcncd/arfg+eBWzsczHfvsSMBqnWGum2PPXK+k6pf6WUVvHKAzsf5f3eMVtaLinX4s5KHxxCJEtY58atn0UeVJYXt4cZtEDRKj7mMpG6mE7fJ9ajPDMzcUfUq+fiXR11GEx93r+mI75hpxDK59w6GXo9jys7HZyTM2O7gmONntYmBdob/K9KJs7NQVkdSZfczZHHrKrNFFbLYfsUXmEftfggqAqcKBrgMo1sGmWMlY5yryCjVdiZXhbPIiaqNTr1sCLvlr9yJ3ravzkMWmGTKiOeKeqcTfj9COnH5ozirnsHg2aCcmVXioWNnVFmWoWsfOIjAz1xPRajzebfL6mTvYo8t4naz++POOeJHEXCrYNJIQdWr3sWhApUO3E49k07OnpxlZ8aGfA+oObA3EMugNvXO8lfV/ObzTl/Geff4quvDkXFBGTPdtnIM6/v4+JAvjY6+IzERx5GEUHpKZdQiqRGff//+/Vcm9q+z11fsOYbtsG5mciPLAnXAPqkNGx4Dhc67eN15Rego8gbty9vb218SMea9sltq7uD3IIyiDbe2sJ94TdgiJJ2QNGOsxOuZbVvFERv21I/anbnb9mg40zFRTrdrQ01i58izwlbtuXoQLNfKsex8Z6epa5TOwhEHwZE4LnjrtLWyhjpZAVgukGVisVFj46AUJR6vyjD23Ntbz5HBc+An3Hd28p0NWd1hr+wMtrNiN7g9tFW4Wx//lc1QDj+W6RIvewiaLKBU9yL+d/RRZT+rdgIclDk/AtF9lITBwYGqexVsZzg4UIEW2xg87trAzyvB7+CxUOklXOdIznQ2Y7vtr6zblXrjv5vT7Ovz9S6oVsSNOr8KJ0eHnMvqQPAjV0qX4X1GoilkUWW27XOWDz++he9BQn3gNnlRd3UykrJ71NmE4JeRV6/9QKKOy+M942QEPo5QL0RX4DmMj6lh+5g5Fv8zW8XrH+eZWkd4b1T2mOpbjHVci38uBuzEutyPvfpi73nEZDz9AChj6YKHLBjg49Vk7xpeVc+eY6uLT0E5g2cGmHvr6vSNy3QCn5VgwBnagAriOnI4Ba0CK/ef61v53kWnrcHj4Iwgzhn1n3bflcO1p45tqzczlK2qyA0+75w01SfnkEc92SPFZ9oeJZ/6vLe80td4Dv/HZ+fo4v+KNOnaASV3BAfqHSErZBb/glB8dtm1eEydx+POz+nai5+mS54JmX9T+YfVunDXKD276pd2dJ+qh4/helPzm9eDanvFfu4h2LLNV3WNuy/VfXTXhB5hwkm1w0S6ehRNbdyqecebtAiVUaXQecH4Kpx/kD0hwd+PyIB6vQu2JZy9hsdRZl5HaE8wSwkzoPg6jnc48wmztRjoM1V+U+ezQ7d815YN8fRD0AkWlGF1SpyDhNV21eLokmHdelYDpNXFuIrVOqsx6pap2uiWZ+PIL9bbIw8ablXPisPljHB2zSomSPiZuIJYeDas6tNuPUwAuQBv2+osHNUO189t8TGuj3WdkqtzfC8qRy+zWyp4dcGsmuPqEQml19X4rtphFcCqerCdFfIJbY2zOeqzk0/dlzOd+MH9oeYjn6sCwjP8Pv6u2j5io3juK3lU+cznCplcHVx/98do8HM2RhUBtYpuPLSi75h84nYU8Y7Xox4LqHcGZXh5+frrfdtW/3ppF1mmDraJGwrRvqrrKDp2ickmJgu5HpQt7ol6eoMzbRFOt6h1ttcP69iwPXWqujM8NfF0ltE+W0HtQceRvgcqo3JknJySzhzYSg73uYujhuns+l0fXBBxqz6jAlZYCQbcMeUIqXNO4R0JAqpgrlv/4H44y1Bn36vjV6DTVmcX+CwZOmR4d3wyHV/VUwV7HTtT2WEX1HT6qR6H2DNvHFFSlXdtO2KKZXZzyhFQjLPmoHr8Liu7bT5Iw3P8Ob7jf/7MZVZtRWdXeuzM9VC+aNzP7B7h94yU2IPKp1Hzbe9Gq6sDdUO2TrC821So5vEZtlXdr+6GgdJn2F9FanF/EZzN5DZt41gAr4n23Mas0mcrWU5M/kTdoStxrLDe7g9lRP3dR+Si7iy+qNZFZw24tYrjmmVexXlFHqGdwXHkX9Xj9vAazHZS/cT5gf+rTX5XV1YuQycOU3hq4uksuN3Vq9u6Z11oELvsfFYH1sNKE8u5HdTKaHcN+Oo1Z93vTj1Hggz+ninajlHcK1NmgBHVzkS2g6Pkc07PHofGORadOicYeG7s0fXPeK+d7lvt/1Ebc/RapcurzxycqXfMOfvEZQKhp+IFsGf0TaGyXayn1PUVkVG1xWO6Arxve+w3ypb5BO/v719exssyc9/du5tYdi7DcnF/1ON6XJ9qQ9XJ5Z5R9zwTFFHC937PvVid7yELt4OfV7I9snmmyvA4qLJvb2+pTYk6HFmFx/BalTmf9WHl10FVP/h+oszYB0WYhT0JOeIvrkU9g+RJlyjftq8vI1+571H/79+/v1zv7m+U48eRuS/R5+oe4Tm24xleX18/kW7VmLnx6Wx6v7y8fLIfUV/McWw77qN6QXym47N1HG3Fd6wLXy6urlV9UXEYv7vQycjyOr3g9IdrI8P62x4HT4sznJiuET4zqDurre+Ke/b/rLYrA7Z6/U+fE4PBI+MKW+ScH3SMXLvZtdn37rmzy5yNR5RrNehWxNqeF5qvtDF2ZuCgArMsaOcgMNNr3To716z2if8cSexIKP7L3mFzJs6MSaJcRRI60i17PxNefwYyHejiOSSdbgl+FE2RJzyOqg78c+Dx50cUg/zBd8+ibB1/wZ1X5F52fZfgy9q5Anvt4WQ8/WCE0Th6XXyPSce7F7y7jOjsrFZluO2VRXbLYOHMxc+s9N7+r6K6z11kjzyouZI5ZaoMHlOfB4PBfYF2xNkiV4ZtTudl4tvmbRHrTczYQf1WvVOocnSz72eW4eNVPR0b0rUzZ9kglUXL2QWde9IB25YsqKzsjPqu6hjcF5m+2Jvxx8j0WnZNVac7VmWHZN9dZkMF1cdMDtc2Z6CchVUiKdNfrB+QOOCXhGNd/Cid0ludpwbcfUa9GBkzag6rLC7Vvy6yubKq7zCDVL3QO4tXM1vkHg/ke8IZTUxuxdgyOAtaEVRMRKr3P6ly7n50bD627ezaGdhT7xBPT4w9CsKRBqv1oAyZQ6pS/zOFyG2tlNnj8HbK37JMF2ocO+N1BGcSXC4dWzn86nxVBv9nnweDwTEcsSGsx5Rj3dF1LI9zUJGgcnYpqwMd2S66JEQWHJ5dJtAZ15Wxj3J7NrSwPbZjAX7RLj4KsW15cIF94M/quyqf2ZnuuI8telwcIZz26KPO9atlXLmOHthTNxJW+NetQ5EhVQbVqqyVvcDP2RizrVI6KMrhdyTV8LFtV6ZDsEQZ9UjaysYAEh7VtR2oa927qLgMkk9RBuXrbDAoko+JPt6oco/rYXn3zicmoz4+Pj8mh3/8Lif1snG+HwwX3yjikttX8RPjiO3uYoinH4KuY71S37blz40zVrKbsnKrZb4rqnHsBEgrxrwyRh1j5YK+zJl3irJTRrU1GAzuh44tYvuironzqAdUEJTpjmrHVunHI0HkKtnRrWtvmTPsNere7D5Wuje7X1gGA4i3t7ft9+/fMmDBRyTcrwmx3VB2pGtn3PWqLv48uC/O3KRz62wPccLoynnmxuDea/E7/2obPqoUY4P6m/X93nFzpLAqh9lJFYnG5UJGJsHjXBARMQ6/fv3a3t7e7Put9vbZEXbVC8fV9d1rAiF/N1NLkUk8F1hXr276cJasux5liH67+17Fn0w6vb+/f3mxuLJJq7GPOqc29DPSCT+fnaiQYYinJ8XKBKmc95V6lALJyB92PLOJ7nZbO4tDtdVZSJ0x6I7TmXWt1qP638EeZaPmwcoYqeCElS1+Vs7DSpnu98FgsA8r+gbL83dlozLHXAUv2/bPkVT6But2+qhDSMVnbIvLZagID1cma8cFWh3Co2NDsjKOBNwDVTfKjjvQqpxy6rPAxY3XHjujzndt0U/YMHs0IHGwx4dCOL9T6bDuvebAnD8z8ZG1qWRVcnLbTs/ytSgLIrJ7MPhX+letg6NrwumilaDbxTtcR5A8THJENlOWFRVtZFk9anz5xdedPq30twLqZHU8kyGuU+QTX4s6XP1IlZMdx8f1i2VHEpKznhgoI89tJpTw1+riO5aPOpStz3yBzA67P4VVvbd3bQ7x9AOgFvKeCVM56S6Y4IWAxzLj2CnD9a8unG7ZTrmzynTgFM1V/T+jLXcNK9DMuXf/3TE+584PBoProQKNivhxNiYLHDLSguvlYKrjlGcOIbfBclTXHC2TyZAFrlX/j4zRHlR2HwNZJqE4GFJBQCAbt9Xy2ffVcoNrwcGq8k333JvONezbOtkUMaSC5PivSLRMJqWf8D8G7M6vUoSXkptlj/XVjUVWY5YsXnBlHZGd1R16iO0JjwNn3nR+ta0iBlEGbAPLV/Px5eXl06/gYZ8qhF7lY5kvHm1m8qh2Qi78H8iIKCxfvZ8R7w3/5/um5AhZ0d4w2aP+q3FU/XfnMhuvZMhQ2V1XfkVfDvH0Q7Ci4I/WqRzairBy9VRlsnJn9bmzmM4qswdH+nyk/J7xdcFOfO44/93AKmt/MBjcBx29UZVR5FSA9RPrHK4Hr1NlWecpOELdyV6V6dRzVpmOvebjzqZHPd06O/JWQTqX3za/84z25MgmTZdU6tY3eAycQZgGugSGIpkc8aSOOeIpkyuC6FXiy8muZHPEXgBf2nw1MhvA5VBnOX+VSW6+B0w8IYER5TgTB++Le59QhYy84Efnsvp///4tX6SdgX11Hp+svW6fec7GZ/euLHW9Am9cIOmEhKkifhFsb6IuPOZeOM51VHCEEs9NR3ydAecnVWgTT6vs1zPhyI1YvbY7Plc7JavOl3LmlTLe066ToVOme31HpqNlOuWvJJ+6cAHDWeVXoOpyxqv6XwV+ZyreMx3UwfVYXY+Pasecnuscc9evtqdw1nh19PjZOr1ay3HeOVbs6HVkusIJPBN77emqLe6SSRUpWO0843c+33X0UZ5VdGxRZ55fadN+KnjHfnXHv1s/11eRsor8Yf971R6s+oluQ9e1w33hPnDGyZXz1433yrWKXFJ1q/Mxfpwho945VD0KtiI3y4EESIZqjvCYcHnnt1e21cmPdXCmXFzr5mJ13J17f3//9Ct1KlOKCTKuR8nafcSbz1U+Sma/lH3bs84ym5ONQ4bJeDK4KvA4Q7msYIXh78IZvWy3A6/ttrtHNrcTcQRHjOIZC12h2onqlnc7IF0mO1PkGVQQwMf5mDNmK0HmnkDa9RE/d196+KiExqNjj45aOX4LdIPss+q/9VzjIKO75px+qa5XzmXof378QaXis+6LP5QHgy8sg+12HktYfSkqy3rFvD26prIANI4rW7xnXrpAj4ODqDte3MvyxLVMSOHxjAxSdbk+V31x49nFkE3H0CV7Ah19lvlJSEaoteP0mQPO14wU4bIVGcDXVfUy+NFXXhOoj5lUOAtdWVUZDvi3Tb/fR+mKbfunh1AfqYwZ/sW1t7e3L1lQ+MMJWT+Z4FAvBnfEkSKq+LFkdZ/c+46cn67aRmSbB/Hn3neGdbLu79gbJpyiHnw3V5Th7CwXx+D7m9T5rO+ZXuAx4czBbPxXoOwl19f5lUGFIZ4GlwCNYYe46CjXrI5OW7fCIzqEnTG65TiuBKVZmRXS6WiZPbgqaBwMBn0o5y0jlzlgUsHTVTqyS0jcU69l7Z1hZ9S4V9d037+hvncC8o7NOurs78XYmWvAc07pjY4O4ODziJ+VBfQocyXLno0QJhayOjuE2BVw7VSEXjWGHRKOyfYI2BXhyNlPSB7xo19u8z7KswzZe5e4LENliDJJj224dYFyu02pgNNfqv8ZYVxBlXME2svLy6dfI4zvFdkSJBV/dvNSZUixPcrWjtp4c9/3wtm1vT7QEE+D0+AmfDb5nSJaKdNt62p0A4Yr2qrgxmjPPTsDzohnjg0frxyBM8us4CrlPxg8MtjxjmOu7LbVZMJKu516OjYEv1drN8rsCeIqqHGsgs7q+iuwakNWfIOu3JxZoGRUf3wO++PKqrr5nHPUz8TYmfNRzUX2RXntV4Gi0lVdQiDOqXm5bXn2gSqPfXNydgJfvg7/32pOrgTmUZ77XelxR8JwFg5nc0X2k5JL/SJdZNVkYF1TZSNl7zZCYJYOv5MI50+MFRJKiiTCvqo+dO9byLrHR1D3OntcTj0F8vb29uk+upeVZ4STG1PVrlqv2drnPsR3VWZlPWb+xpFNtyGeBqfBGWa3QCqnqeNYPQrpxLiV099BNkbq+C3G0Tk0mbJ11x35rGTKyjhMEDCocOv5cMv2qmAJ4XbF98irAgkMmrqBHR9z3+N/h2zi+vegIjPO0n1nILMh6n50ST1Xlu9FNo+U0852xdkedx7LqXbU+bMxduYcZPdVZXTsIbe5TEXMZpuF2TxTgWEVrDq/3fldVR/jPD6GjHUeDYidDKxzuI0MWXnsD7fH12GfkYRSNgQfiYtj8YhXkBdZFmfUi8eZyOr0B+WIdqNe/M/9jj5kawXbrvx3vqZja5kM4n6p/nfstyLrfv369fcv7pXyJRzphMewjczWVD5AZ+0ctRMr962DIZ4GpyKbjCukVFbXaplb41ZOZxdHxrF7fq9ic45MFRBUn1VdR8tUyMine87HweAW4LmeBeoZybPaZraL7QI8dJw7dbNDjZ+do891VXC7wp06j+jHs5DZmSzQVnWo+6iO4/mK0OSAvbvD7M4r+VzAcBaqTbshoo5BBdfb1rPfag4yVsnqlYBd6V9Flq34JO49QU5edzzTkWfMWR4fvm9u3VSfVd2u7bgWdR3+KpoiZZCkise4gtBA0orbUrorjrv3QmWPlLlMHfW3Oj6qD5WNUvJnfXIZXUzIdd5JxOOJ18c9xfdz8a/oqXWpSD3MLsMyPJ7KDuF4dNY5zsk96+1suzLE0+AUOCNbOURVmSyYr+q5txN2S6e/AzVG2XhnO8hnoBofdvY7Thd+zspkbarvXYexO18HPwvdOXAWQXnmnFuVKVtXLvjZ4whh3Vk9mS2Ic3jeOdddUkth9f53dZ0r2ylzFTq2uGtn1Li78y4gCqgX5mK5ytaoczxv1Gcl21GMnTkP2f3F746ccP4rz/HuHKjm/B4o4rySYS86+p/lOtKG8mOzcc/0dkb+V/MgmxPx5+qP491fucM+M+kShEbUE2PQedwOyQ9+HEzZxyhXobJR6n6G3C5ryf1CovoeGV1s67nOaFv1CbPP4vzv37+lnXH9d6QTxzp8Lqs7s3tH7UKlh/bqqSGeBqchCy548q+QUpWxvMWO35Hg6wp59gRqaowyR+kWwUo3wHJOYVXH3jJZOYVs7K4m8AaDe0KRDHic154jjVbbzOpx3zt2KLMh6BAHOr9qd0Zw1S3T0aNnomtDumVWN6O6MnIQ5YLX6pyqF8+58rcgn5SMgz4UibFtfk52CYxupgXWubpOKnKL9XNGauyZS5m/nunVs+atW2sdMl+Vc34ylu8Sis5vddfzL9+p612GDPZF/YKeqwflyvrd6VtWplse5xJmf2Hf8BFBvEatY8y640fx2J47WXk81aN2KAte4+wE/6kyLE+22ZL5MStrjddzVm4VQzwNTgcvDjV52UBVOwHZAnDl9gY1Z+JRnMAj4901FCtj3TVEKwbrLCO5Ug7BjuDeegaD74yz9DKvtaqdTpBebXbcYpOD273C+b8KHVtc2SIX4PN5LpPNB+XQ4znn6Ls+Yvvu3FXjPXbmfDhigJGR6yoY7AZvqk2nh7p1ZaSIIy2dLB2odX21D64C8y4RlJXtkE74guo9Y+nmEM6jzkvGlW5TmyPqF/ScrPyLdRnO2HjJyEHVRybWHHkTY6EemYvj/HL4TGZ13evr6996USa8BseUj3dIJ763bu460mmvnbhqDd+ceOp2/JaEwRGj7RyiTvmj8pw1Rh2loJw9Pu4cwkzRneHkZ22t3NtsPB/Rsesqcy7fHZd79blSvNU1fCwrk9VVHRvcDrce/2e8348i8xE59gRYVX0ZMYXtsH7skPNYntHxDVb9hzNxlr/i6u3Y5SoIwjrVfemOGduFrqPPx5U8zs6sQvk/DmrOPcr6fxao8XOBXlaHC4oDmG1SBbaZfAjlk2dEE5ZTdSu9ezax7vyqlTXMhNCqf99ZM52NDBxvNUf4nIqXuK1VfZz1P84hOeZIFq4TyZwukaiOZ7FhBu5XZ42xTseySqfjo3I8LivAOiP7SclQ/dqqkjmLfxTJ3YmPV3AlBzMZTw10lNOzGv0uAcEKy+38cJmq3kwZ84Ls7NKsyuBwz4Cgg7OCu227bV+VU8POhEp1zeQ6qwzLyNfuwaPPo58MdNRXAoJ74pYbMl10gp09Y8qOfhw7ox4+z22sbEJgur27Tv1E8x64wGl1XLpB21myqiAmaw/Pr+rjLLjMSCbl7FeBTDaOZ61VFdRWpETgkXXZo8AFeXgeiQYXXKr7FGD/2bVVvbcm6n15eZF1RvmVQBqv4/V3dKMcSQ/W4arvVXtZBhDac/UIFa9VfudPdv9QZtZN2K9MH+M8imsxrlH3IfucjQPLHccV+VTNwW37N+7qvU/Kj+JyKFfEk4oszdqJ49GPTM+r8fj4+PiUnfTy8vL3XUvxgnDuRzbenXdbqWve3t62t7e3v5/5vV7xp8ZQ9c35SM4+ZePE9am5iXXvxRBPg0M4Qu6c1VbXqV2pv6N4BvvRCfQ7iu2sMoPB4PvD6XjnILvvmS3qlDnDznwH3XerMeqQVVmZs8bx0e/HT0VFbDsiwZGrqu6uDB2o9XEmrvLrbzX/kdDhtuO+nbFxzcSRO5fpODe3tu2rHalkUrqSj61k+GTvO3I+fNWfSs6A62fWlqojmwtYpmpLydSdz9EGkklK9oos4nlQ6SWurxoDRXK6Pu/dUNy2IZ4GO3G14cvaygKHI4uB68E2vjPu1cfMKXDlquv3lBn8bMy8+P7Y47xvm3/MPIMq4+rYY2fO1I/3QuU/nGWLs+DIOePdAOssWQaPD+er8LkAZw3cQr7V+XXrOXiv+b9Xb7hsjzgfBI6bB0x6VBsejCOxjGoHM7xWs3WYFMnmviv38fEhM9FQxmwtcb9U+12bwcfVeKgXu++9H4p0UuODn1WZLAvP1aXKq3Fz5NPZcfa2DfF0Fzy60s3q4Yl/9a5IRjjFebcYVhbHmYvqFnh0+bo4w+nPgouVegY/C24ePMP6H+RYsSHsuFX1Oqc5K6PaOWOeHdGPq/UfhQoK3M78WWPkAiQVqCuH/QobMrbocVEFudX9ijnNf+56pUtU26oNF/TvQZV1eCYqsuCqdtQxzuro6B08HmRO5UvsIdIzO6L6kNXjZKvgyBA+x9dwWRwn958fR8/uXxYnZP0IIGn48vIiH/PkX807iuijeoTOjasqt21fxwdl5F/RU3JUcrr2Kl+qiyGeboyrlPmt4JzHqxwoN9lZHkaW4uracXjW+/WIc23FYJwVVI2jP1jFyrqZOfXYqGxIHFffXbr5ahn3HbGqq4/ovnvO2b02fdv6wVI27upz5uRnn1fxKPdg8A9VZsqRehUyn7VLUGRydggyJd+tMrPU56ugSAPOQlu9T9wHRVpxeyv17iWbHGnJc5vnWQZHhlR9cbKrz468OyN2q66rsm7do4Z72gziiUkmRaZla9jZr0C8t8ohS+BQiSXZfdk79kM83QF7jMJV7XTbcpNypZ0uHLmVtaV2SfcQYkeypm6J7hy6leydtjLDiOgGDVWZlXorPBqBNzgHmbPXxaPqiJ+Mjr3qOPhXQdmZve1fSb6f5at0bHpme1fGZ4V4wvIuAOjWU8nU+T525r5YIUErrPjISIasZMQ4OVd9/yzz5wx05v+Zc9/5gN0N6c7YKxvD7ay0sapPjhAz3esV8eTqdMCxyNaEmnMdGZ0trVDNcY4h9/Q9gFlI7sXpXZm5v0xidX7sxK0/d3/O9ouGeBq0kTnzV7fXVfJHgsDM0X1Gh/AMA3slzgqYrgy8Bj8Daq2sODCPvM5+KjKyA8/fGnsJFbw+8Ai6bzUIuoVNXyWe+LML1lwdZ8k2uA9cFgD+0tTeegKuDhVocyDI89KR5kfm0lUbyYxbzf+MfML/6rrM/2c94OpZCeCPjoEj1KJe/lXBrr7mjDElZ5ccQjm5Tp7rjnzKMnP4cyb3CvHGxI6rMwMST/zn6nJ9VX3BP7zXPHZdQjTKXbU2h3gaLOFqsmkPnOJZlfXItT8d3eC7MsZXGebBYAWz9gfb9nWH0ZFGVRlVZ/b5O2OPrVi149Xn7jVV2aMYu/ZYyOZZlzjKyO3sfncDwwrd6zMdtVLPEdyC5Kr62el/RupwXYroynTYCiniSDR1LZMa1ThUbbnz1S/kZUSSkrHKeKqSHzpjEmutu2GYbU506lAEVkbsVEQiy6FeXM79zEjUztx2fduDIZ4OYnXgO+U7xmeP8jjLqb2lg3xPZ/ynBAK3xlXORqfecfQHDoo4cLilXugEPN8dR9bto9uQM+T77rpvzxhd4ZudiWe+Hz8FnSygam5m56vsmzOIqD2oMjHObIOP7am/s2F5lOR3G6eujJJjNTNUbXLEd0dCZXKuxoPdjWC3CZO1U5FoDu4exvfVbCiu2xF2ikDqwJFOWb/we9aWqussf/EK7iBwKvF0lnCP5lB3F9JZ6DqrnXoczu5DdxEeMSp8D87eqckUvTMcWO4qB6FyTjLcy7l1ytkp08rAVY5fVcaNYbaTcQQu1XVwHEccpg468wh1dOZoVjgyJ1ad5z1yXmXTj+y6Zrun7p501r865mxOtq6dI4jXXmlnYge02mW+Jfash65D3W27GzR0yIYjcnXnYmVD3TwdQutcvLy8/M3qiMwChiMK4nOm7+K8q5vLcrv4f3XuujqiL/yolcvEWAFnYMR//lUzXifqmvicBfOV/8zj7u4X39OsvZgzSp+xneoQDiijugeObMruU/XibGW3ArEe1Phm8yPTU8rG4XiqseLrec5WNgTvUzxW6+SOunG+qfuM5R3pVNkfp/tj3CsfIz47nw/vH7cV/7Et95J+JUMHbeLpu5JKXTyr3PdCZ7webUyV8laLyyn8W8r3qNhDiFak09H2Bs+D7vw+g5g/E2pzohtEHCXQO6SCauvW+uTRdVegc++O4NHtzHfE0fs5duZnwwX7eB7/M9T6ZULlnnOMSY2rkI2PkkGNGf9cfDZ2iqzB/y7AZ3KIZVDnHUmi6mWiUsmsxobrQJlc/zO70fHFXR0rxENl3zrXd8ZKnavmM9+TTv0Z8ZORSxVJip+zOYLl2Z9Q8/UK38GRnR3Mo3aD0/FMpJMLFt2CcgHmLQOVRxk7RteIVZ+relevHwyuRpdMHVyHPXoxCyS793D1Xl9tZ55p7l1lz1aDlG5djzK2jyLHT4MK4tTa5fNxDDMN4twZ/uNewmiFLODrVtatew+QW1uOpHPZGfxZXadIJ66HdbMil1g+zHhh8kDVmcnrzlUkkxqHLGbI6j8yF7vzWdm9zng4e9kl8JwsHSKw+s/HFGHorsvWEmclOSJUXae+Z3NQkUmOwDtCUg/xNDgVbrKvlumgo9w6bDeXzRZypgCvcAYdq/2IjqdTaF0DWxnnVQLq0Qi6q3cTB4+BRwxUvzuUw+++Z8dXyKfKXnBb7Nw+kp25JVwQdIV+PBpgdTZKzpK5U093zjz7HHlEOB2jHkPZNu+LMHmlgtSu76Lu+0omiZNZ1ZXVW8nr+uzaV/VmAXHHN4w6eO3yd9dnRURtW008VVDrtiInOkRJJUdXNzoyjs+7eYxjmumm7FrVXzdWKn7okk7VWKkxi/VfETZR1s3XinyKOqpr1VxU/VG+Rbf/arxW9MzDEk/Voh3D+lioCCa3GPYo6i4yRaLKOjiW/RZgBfFoZAojM2ad7+7cKun0qHj0+zfYj5W5PTgP2W505/i27XPEVvRyFihwW0fnzSPOu47eO9u+ZYHpWaTTrZGN0TP5Cc8CDma7BEO1zjlYV9dX+gqPq4AwI6+7OGseIWHRJYni2FX+9+rYxzF+pw9mc7n54fSPax/Pu3WdjaNqsyIkq/pUXRWBGmXcGtkzvxxx2CXSUK6KmHLtZ3U7u+PIuTi3d63x/Mh0VDV/WI+oc/x5z0bHwxJPg+dGh1A6Qjp1Jnp3QWRGXLXF9V5hGKug6NGCixVFlZ1brScrM8734BbIDPjgNmCHq7PBoXZQM13uHPeOnVE24xHszCNgZRzPaGfFhnZs0Vkyr/o0Y9/uDySR+LiDCkw5GM30F35Xa4ezEa5YU5msnWv2tsXtdXR2tTHRIQndn6pXnXcERKbzunMq00PVNdnnqo6McO0QKXv0vdLhTOZUcm3b10c/s35XxJa6j9kad/Vx5lQHyh6ox1o78yA7rsgybn8FD0k8OYYU8d2cr+8Adc/Y4e+U6aDrNHbmUWa8laxVwHAGWPYjjPgtoOTd+7lTD7f9DHjk+zc4D88yH78jnPPlbIxzGDl4y7B6v4/amS5B8WjobCqcLXcWLOy99p7gQLsiKAbHoEgKRyRE+a7P+fGhfy2qkqVDYHfkztrgvnT8sAydfrqsDOwPEn2O9OM6+Z6wbncEf1z369evT798xqQS/8KbIqeiL9gnRzxWBFrnHnZjH/U5vnd0SUbyrBxzpJEjC+MaNZYKbr1yO67fWezh+sbn3P2O9uOXaF0sxFBrv7rG9Snze9TcdOPV1Q0PSTwNvgc6i2HPgjkTmULpKO4rZX50ssnhCEF0tM+PEBwMfi5m/t0eytF3xNMqqX0WMod2xc48+/zau0O6B1e282j34Rn9hO+ALOBVvq0jHVaggtPOxl0lo5NZtbEKJS/X515AztcH+aPIA0cgxX8mmxyZF//d+5tYbrQ/TFJF/dnftm3b29tbOZdWfOgugaGu53rUPFHkZncjoUM6YTudcXR1qbbUfGQyxRG4VX/cWLhz3CbL5cYGj2cyV/I64kwR7Hze1VlhiKfB5djLnN8air11O0dZmStlejZ0dj+6xvSMMoPB4HtD7XDjf4TawcPrrtApj2hnvjPOvodjZ342nP/COscF5h8fa5lOCitEhCubkUxnowpasyCWdXJGCmQEifuvxoXJKkVexbVIPGF21O/fv/8e+9///d+/1+ELppGEiIwXlmvP/dhDQLjvPN6djRJXLiM8MuIpPqt1g2RStkmTkUYZ+ZTFMHhetVtdq+Tg7x0CKnCFz5KNwV5d8ZDE00837N/BwXSKGs9XLG5AzYe9THQFpwyyXSTneJwFpai6/dorR1b/yi5Gdu9w3LDcXqes47xUxqjTt47yd8evdvB+Ch7BRmRzfG99R/QVOymq3C3G7Qqdo+pXgR7vUGMgwO2EnsEAIJzb+P779+8vZfbKnfVH1c39W2kn2628Aiv1Z0FlVaaLTAdnu7m31Ct721oJSMfGeFTkDZ5X82NlbCObhetR+tvJEajq+Pj42N7e3qS/kdkHRbK4cuyvcTsqe6k7hpUd5HfhKLImk4n1DBMPaEf40TocgyCW8PuvX7+2P3/+fLFD/+///b+/9yXafH9/397f37e3t7ft7e3tU194fFEeNZY8ZtXYqDHH/3gt91+NP+oktGdKVzldG7YWwfI7wkeRNqquAM8FnAdYpopRkQwLvyFrl+VlG4QyMAnm+rXiN2Z2LvNBlN5Q972LhySezkQ1GLcOYLo35zs4DC7Idp+dknKTnq+56l6yQtqDK+U7iqOy4fVVPays4prKCazqVDgSTFRGLGsXz6m5njl2g8fDs92nR9I1V4xdl7x1jnOMz69fvz6RTXjNLdGxL2fYoD3otHn2XDuTyFU4Ssp1bEN23ZVrU9nUZ9Nft4ILAFVQuW062I3jXC8f3zPfmBDBax3h4Opf9bUU2RLHM+KKy5+pt1Q9bqyZeGESgYkmRbxhWXWM6w/SCTOduL1t276QgpzphHK4fmGfmZBR47RX57i54tZO1dYeOTpzSJFPFVa4gWqdHdXpigBVxBqv344MZ8mZrb+sjMK3J54Gz4PVgOnMhX82FLnyqFgdx8wAunrdsavH5R5zZJWQGgwGx8G7lZjtpIKHcNzYgQ9CakWvrcLp0KpMV290ZLw1qbTqJB9pvxP039oW3RJjZ2pk6zt8UUfwKEKAyx7xZ7ftazDN9Tv541omM/D/mXNdbQQwSVdtDu5Ftp6ZdHIyZGsE7yWXjzoj0+n3799//zjT9uPj49PmxsfH/2U/xXUBJKHCDmG7qzor2t2j6/keOiKki0r/u/l/C1T9wTXHfyttrHx399tdxzGnqjebT3vHe4+teVjiqRsAf1ec5Tg+GqpJusexvoUDuVJnFiycJVt3flTlMtmcQxPnKoOtPrv2riCIrq5fQTkorswzrt/B4NHgdqirwMMFcPEIBB7LsKqLMx2qyqjv3x1n2/SVoOfZx7hjgwaf4XyUOJeRPHHcZd9k17l6VMDP8qiAk/VF5mucqduyaz4+Pr5k/1TlsWzHX+KAnG2A0/8VcKxRDnz0jd/rFI/aKeLp9fX1E3Hz+vr691E6lid7JA4/uzl2hh5btW0dWY745VfGxVndZxFOHfKpIvnUGFey82cXGyodsoJvn/H07M7BT4Yyinvv5y1Ip1UosumW7L2TIyvTHUd2sNRuXLee6vNZeATSSe1ATlAwGPzDkfXANoUDgyzwwHc0YIAUwQKXd98zVHqTdbCzId8Zq7aoqqv7/TuRToGxMz2oINIFXqvkUUUcZNcjlG7oyF3NgZV5oWTKyilSDPuC+q9aqy7bCMtjPbzpoN7Vg/Lxta4M+29oY/78+fOJcOL3PMXjddv2b2ODbQDbH5aPx8r53K6fewhpd70r161PXbOXzAmcTbjjeFf3YG+9qh5FPjF4Ywyv5199VDJm93PPOK7amYckns40/lcypFfhGWXeCzYkR5TPXoXQGcuqPmVQnRE7A9050pXb1Y2fs8BJyZQp06ytq8ZIfb9iHVWK2+3ADQaDGm7dYrAR//F4Z9c7AoLYgcbg0TlzK7q4Yx+qMt22Hh3qHp5l0zvXn93Wo2A2OHqo7vGROdCdS9la7ZI92XVqA4w/u/IqQFYyq3Xk9Kpq1/l9WE69rFxB6VG3we3IBFcnyoW2Jt7rFNlO+J6nwJ8/f7bX19cvj9VFfUhS4QvE1RitkjyKwFB6YZVoVXa4O5fV+YxoqdYQltmr71Zik9VYxZE/R6Dm7h4y+Uz70K3rIYmnwfeFCxhwEXQW5BG2/Ao4JbwncLhSpqrMXhm796wjzy2gHJQj6BhyDoAnILgOZ5DJg9tjdU1Ua4oD8IoIdtefQYDs0X179NNVhPqtcYZjflXdjw4MYr/DXLgKKoBkkniPTtqz8ZkFp6rOTjtdEmoVGUFV9QO/O0LF6WlXr6orI0EU6dTRF0w6ZRm12/aPRIpspnivUxyLMio7TsUTe0mn+Lzqd7r7Fsf22pqKuOSYsINqw3evjE7Ws2yII93YJ8mIU74Wy+/VR6rOM8pv2xBPgztgr0F3eCQSShE5z+L4VeOIfcn61b0fVyhxh1u2tW3eyZud6MHgOjjyqEM84fdt65PpK2Adiu2pMup7t50KZ5Kzq6RaFbQcsemdnevsmu9CSs0Gx3G4jA43pmzn9wR+CitEWDcLYm9gqtpTnzO5sE1FqGTBNJdR5JEjv+KP3+PHG5H4p3QXE1D4QnEEZ8y+vLxsb29vn+p0WbUs9/v7+5I+VvPAXe+IHjc/+P6tYIXI4ba7RMiKPco+nx1bVvO8Ip6q+lQdys4qQtK10cGqnRniaXBXODZ3Dx7FYXwmsskhc9D37JgcLXMW7jFHztxpHAwGn3EkuFaOXhd7dUlHh34HG3IWjozzaj2P4kMcwRBO16EK3q/Aqi5QmRF8/kzcaiNvD/nuSJz47MqpulAOJJ2OyBiofsyiI6O7TmHVzjny6QgqknJP1tOZcGN+dBy6m0KKdNoTJ58RW5+NXcSTYp0dOjfpjF21swb2ihu0Ml4ruOdkUmw3vlODz1X14O6CWyhdllp9Rrky9vcsHKnz9+/fpzDtHWO1Rym5nY7Vnbg9be6Fm1tHDFpn9wKNB+6ITWCgkTkacU69gHOl7r0ynY29O4ZVfbdGZxc1vl8BfmSB35ERO8y8HnnHuxuUVIGdQrXjqMrwz29X9a7YXaw/QzcranUMrpz77l6qsTra1pXXrMrIj/yo4HjwD8p3xeNM+Dj/sfIruZ7OuuP6VJvoM/Ocd/3ha7mOKOf0nqtP9dv1papb+U+Zj4+ZQOjjxXul8JfkMr2TkT6qP/GHj9HFu52UjNu2ba+vr39t0uvr698/7Ed8ju943I0djhmWd+VibJQfiu28vb3JPvN1Tt9WOlfZWSd3V5fxelMyuutWfs2W++Dg1lic47L8eLS691EnvjPM6SGWM5v7TidWetDNJYdl4omZN+dcZjdj1UHqTN7VOh8dzxyUZsGGcj5xLlXkE9e/GgBwu4+KW7HUTkkfqe8IziQFea7h3KrADtBKeWyfnSj8/Mjz71Hg1gE7qlWZZ8Gt58QZ7WV6+qxg39Wt1p0jkNS1qrxz0JxdO5M8UXO5a6/O1uWPio5tdM716vhccZ9XUcmsAjxldwYaHNMgOmtP6bhKL63U5c65e9vRWx8fHzKwVHowzrs4y/lZWd/UcYwBVB95TCsSgYN97h/2c4++CGKGyScFJJLe3t6+kEn8PWvT9R37m5ELUbY6ls0j12bHV+O6VmK4Ff8d5Ytj1diqfqz4MJ3zLENWfya70w/VnN0LRzjF/0uJp8GgA2Uk3KJzCq5SGKukk5Pp0dBRyo8ut/r+DGDjePY4P+J9eyRkxm0wQHBwoUijCAjivwpIlLPZOXeL/gXcGuDA+Bl17iq+g50Z3B8ugAooslpdq76fJY+STwV7nEWl1siezUy8buX6PePjyFOWB//jdcq/V7GD60M3hgiiCDNrX19fP5XD8yqT6e3t7e9fZr+quKfzPetrRQThsez+s/ydWE/ZYG6fr1mdg6rOTh+usinZ2OJ5Hu+QqSJvKz0W587on2rzdOLJKelHDd4HtwcrMzyudh/cAsiUREeRrBqnM3DWYt62vpI8C2cqIVXnatB2S31SzZG9jlZVdnaiPbKxmSDztng0PYPggAjtCz5KwGX5+pDNPdJwlkNaXescZXd9tonyiOvklnbmpwNtzNiZHFmmgJpTq6RKxzfsbLjw/cSMJf5lNPc4H+tM5+co0q0T67mxWfX79uiyIz6DI+r4GP6Fjdm2/3s9Br6wPDY5AkEw4X98lE697LzS9yt92nMty7BCSLFd5vFQcvJ/NUf36nhly1UZRYA5ec70UTtxMI91pR/w+F75um3y5wq7M572sOCDn7F7z8qis4tUsdNdAiYr52Q6A2fW+YzryTkxzxQYXD1HBjXcc+XZscHPQeagYTAQj0DwewdVcMNOKT4W4f64jo7cVdmursxs63f3x76DnRk8HtzGOhM4FTmlMm74s2u7EzRiufhzQaGTM/PH1f/47HTnmXa6EyTvCaxVrJGRbpn8TDqhHEEo8fvW4j1OTDThn5ovK/FNBrQNyu5lJFuFbP5zW87mqj44WfdAkUqVL+HmnpsflQ3iccrIrxW5rtpcUHPGtVWRcYxd73hSZMFg4Bxh/NxdfNn3TrnK+Jw9b89cC06Rr+5arARFR1EFA9227k0s7J0j95b7uyCb71et3UGOK/TamfXx43RxDHdc1frkF/Aq4ql6setKn7q6L3N643y1sdNpq1vmEXXb6IAcj3rfHhkrBEoWlMZ5R+BUbWfECpNOeP4M+5jJt0KMdevtyIGfMXNGjUuUz+Y/604m8DKZ0UZgHdv2j1x6eXn5+5LxAGY0KaIpjnM7SnYVS7EsCs6H6s4bvidq/qnjmQzdNuMaRfgp8PjwZ/zhDtemmmeVnOr6Lvg6JUf2wwSZT9AZ61UcsS+7HrVz58YZ6KG7M/HMUEqw269Oua6R2LbbkAJXOXpZkHGGTLcKKruK79YO863nSMcJ/clQzuSRYH9wDI8+1uycoWOqAgx1bXx2RNNVjlvVF3Wucubd9dU1WZlOfff0aUanfgaTHzMmGo64QOAvDFfj6YLP7vgrMknJ2q2ramOlDkV4cdlsHa76rer/tn1+dxLXrcg7lpeJJn4kO+sTysCbGlFXyPf6+vqJfELSKf67bKfos5o7zidSUOSSOueudf6qsh3V98yH6+rvFZ7BxZ+qT2redEg2J39mx7ktdY+P6m21Fiu5FDqxEftY3bp3/6odfh/DNgioycrkCaMyWt02s+97yZtbQxkaHNNnWGvPSBTcao6gs/LI8/DeUE74M8yjwbXoBH3b9pmAytYb6lRHNDmn7cr16wI3dPCcjf0peEY7cytgUD3jkiMLPFWgnwWGrE/w/4ocHflc8NshnViHuOAZiRr+mfcAvmR7T4DLfajqcDoQ+6mgiLPIlHX3XcnHMnDdv379+vsLdzx/cIw4qzbLZunIFGWycd8TZ1XXOGIPr1FzbcXGrs4lt2GZrdsoo9aIGvtMtqodB+V/8HlVJ5OxThZe2x2odeb0YheH3/E0GGTIHGKlvI+2tUeOR8MzEWUVnklHXD3WPM+faWxuhY7zPRgwHDmf+SlsfxTx5IK7q7Biw86ym98Bo0v/wZEIgz5iTfFLkbuB1gr5oogl/ozfmXhR+isjpTvZGliW/zL5Xb2unLpuz5ytNvKyPuzdBAwZX19fPxFz/I4nRcJs2/aJcIrPIQ/Wz7J27IAiXbgMn1fEkKojky0bSydDVu9eG+f8gRW4a7GfPFY8d50v0iWguujqIqUfqnozkpfLdNEmnlzllYI9wylSk+inG9NVg7bn+lV0FErF2KsyCkfkV8b1KFhRZA5Et764thqjjsE5grPmSlc5qXmz555VO0hdGZwDeOS+7r3+p4B3nfBzBAOVwXsGG9HRh9U1V+LoBtOVfoFqB3eZox2nK2O3WwVfGBxUba7I10XXdqsA9CqZHB59E7IKjPH7I/VDyaTmMs9fnPM8vwf/oMgIRxQ4UsaRAF09rtrmOtwmJMrk/KOwl3wc23VBM5ZnefBaFdjymK3o+0qfcJ1Kx1c+AV/LvwrI/oUiFHhdItkUn+O/0jcfHx/2MTv8r65DoIwZHKHl2qvqZJnxMUge37e3t79lnU2t5HdrjT9n89PV6+pSmUQ83/nXCLmMkl/5fai7OYswA44963/XR5bH9ZHH2z2amtWlsJTxtKJkr3COVzu3R47vFgzeqz94r/bctz14pHt3Vv+razlAd8blXmB9sTIOVwS8jK48LnhlA9fp76Pcm2dFRrRm838PyaOu24NMnkdbs4yr9erZ/Wc96HSxc/bvSUKsjjUGR3uuPwO3su/cZoZKniP3NtM/e+Xp1pXZGJ4L3XZ/KtSGEgdVKkhW1yG6c5NJJwdc23iPO3pT1a+OucC7kgk/O9JpFStrisfg5aX/AmY+lp2riKf4jkQTEgGqj9V4VfZJYcUOnKEbWN9Ufld3nmWEyMp62zMHXdsZ96HayY5lNhMJaCYVq752fQFuvyJ7+dqj/sbuR+2O4ohSGmP6HKicou+OW5BvPK7fYXwzA/wIWCH4smDg0fr1CMgc5e+qQ8amnQO3LtkZc07jkWDpnrjHJs898ej24VZwQdgeYuwnowpyKyLPrb0qsM4IILWhkq3tKuCvyDKHao5dNb86dl6d7xCFeMy10SEElRyYEYLjxIRmh6TrEk+qvb1Qda9c44gVRzi58T/alz024qpNcTemivxRZJAjnxz5cyvSEWVYxc2Jp0xBrjBuz4xnl99BLYSrjNQj1nXr/nd2yW6JTpurcnV3SM5oq1NXN8DLHJrvuv7PgHOWb+Hs3hKru+RX4dnHMhtHtRvedYDvReSvtPco6+OWOi2731fIcLZvcGZd7v6f3d53hQr6MpJi23SwiMfjPweLrj5Gdz6rNYftqb5kRFQG1p+ubZZhL1T9TOBwJlJG2MUfvhydx9ltVLgyeDzILpbNycx1dh49c+Pr5qIjJRzc/TxCvMW5bL7g9yNESlZ3BXW/XL0ZAbQqH5OF/Jgi23ScK1WbTo9hne5/Ftu4NbDqA9wt40nhOwUWPxnKCVo1dM+2a8tGYm//V5l0Ve+jrp9nJp2cMXCBECpid82zzfFbAsfoO5BOqw7cQGPvriQ7Ro6EeqZ7cNTOXiXHrdq6mnR6FjDpUQV5g/9DNzOgWmfOD+AAnNvNiKDqnlU20fkYe4P4bH51ZV6Fkj0jcBw5FL8yVxEyzq/DfmeESVb379+/v5xjmTt6Lcp39b461yU+q7IdWTuEhLtuRY6jUHMrI4/dXOA63Tkng/qv5p/yVyqyKbsfq8RRtL3Xb3oo4mnwffCM5NGZuFX/2TEYJ/MxoJyDbZuMp8HgHshIYvf9GfBT7ewz3qsr0Nmd/onzowMOMrNNvNVgrRM4P9N9qQJcHKcr1iYSA46gyQixyMSp+uGQvVRZteeuZ8KQx82RiYpwUKSoaz9wxQZ45zq30XMFrtR7MZeyTSx3jM+zjEofMVnEG9rxP9MvlZ5Ssrh5zfNz73q666N2TtDvHJx1+/VMRmnb9GKvdmUc9rDkWV1n1FPhzP4fab86thdnjOMZxmyljjPXGt/LbCcz21n4qYHiKngMv6s92Lb79e272aLK0dtD/j6aDlV13dLOPCL22oez7kfXfpwp00rQ+RPnRAcu6FPzSdmhK2yTCzZZRiULf1dEy2pQfg89uW1fZT3DBiEB9fLyIn+xDNveS5ZU2UeOxFTtKELD1a0+Z1kw6rqjmzFqjmbtqbawPD4eucoLrJAiLB9u5DM5mJGcCGej8VwllyKw1VzhtZKNWaftLH7B/3yuu07vmvHkbka2QAePDTZsP80p5jl9FdGgHNC9Dvgj42rSaVUWNa/d+cE6snF79jF1O7WDNXTGMQu4vsOY38rOPDKOBknPDHX/t+37zO9bIls3/P6dFWLPEVsdeVxsFO11CIhOOxkcyenm3mr7rj0nHwfdikjb4z84IigjS1COjMCqcMZ6PdufWCUnAx2ClL8fJTAU1DxaXTOKyFVEVNRfgcdGze1Mxr26hK85c34cqethHrWrmNnBc+GI8nh2J3qv4h54PIoeqOZm5pR1dqd/On4CoTp27Rx0x7EijJ8VY2f+4Sevp2wT5DvN9yvgiIzACtGUlVvJ2nH3bDV4rMj3VXkqcvOqNagIJ5QL287GrduWIhywDW67Iyt+PkIaujmwhwzh65Wf1ZEJibcumanqPuse7oXrMx7fS7qoe6/6VK3LzOfpzh8Vk6xuzh1d68vEUzbw1aTfE0g8i5N+toxn1beizI7g6IJYrXu1X7cIXleU7p56nGFwuzMdma7GLdfurfXE6g5a/GcjP9BYcYaewU48unz3wqpuPmuH+MpMVP585vzs2Ic9tmg1QMO6ru7bCs6UZc98PNL+yrU47i6IcecG/8YvAmcce36PSyfe4cBUBXWILPuB2+zYQkc+uA2vt7e3L9dhffFCbqxnr47I5md2XPUhZOKsI7xn1ebgtm2ffo2O5Y42ok4eG0ckKOJpZczcveC5hH6k0r9MYLy8vHz61bRsY697r3CMsH7Xn6wu7COW418M5PPufnfmgSMZld12Nq6q38nOcuJnde8CMR/deLy/v3+6D6ov3fvr4meHlXl+acZTpXA7ExAHahz3z7ilQ9FxZM+WpzKcXdyCdNq28+7Hnt2Ke+PMNdrpf0cpnjkfj9Y1QUAPzulWwDG9Qv/cAqvGfW/dCrfWG7eUpzMXrpwze8mIVd13BTrONH5+BPtzFpzv6eDu81FibxXZhtN3uj9nInvhc0WmKlIgu/fuGvc9jjGZhcdd3apeRRRnJLm6noPdbM5lsuydj9zvbAyzMcJxzUinjPhT7Sl5O4QD18/3x8lY1ankWCXCXL0sK5KUai114WK9+O9IlYoncHMlI5+4H1WdfAw/x68aqvXs+rPSFsuvyq34O26OZW2v4GEetRsMGM4ArNax99rBGvYGWz8JMyY5svXqzs2YDga3ATvk9yB7x84MzkAnAMyuq8or/xXBpFC2llxgPXbwHxy50CEQ9+qxKtOmS15mhIS7pgsmsTrtVPW5No7Wv7L2qrKKLHXXKsLGEWcV4VgBx6g7Xkz0rciibHb33qzew+54DPE0aOERjNqZCi3bOdmLs8bozLE+o64VRaXavWKsb417jeNPQ+XUr37/yfjJY9Hp+3dej2fboqNZ6Gfej7Pu23e999v2vft2FmKM8JfOHDoZNK4Nl6kTmSJxrJNp6AJkJdNRXJ3B1Gk3xiXTQ3uzMDsBeZyPjB6WS9XVab9DelZEpJoLVXsx17ktnsvVmLKcIQvXz/2s+tTtB5NpcazK7uq24WRX47MCN8e699IRm6qflc0+mxhcsctDPA0eEkcW95l1DNbHcZxejZmPx7CyGzYYVJj12MctxqrbxqzzwRnALA38cyQHX+vOZRkWqn5FnigiYIVoOBsc0F8hR+dxIqcj1L2rguQMithQ7angnv/HOSzHxzKsEFrqmqPXdvyujJjlvncz01S7b29v8imYo6SQk/UWqMhkh2wcq7E4m3RarXOIp8HDgo3IXkVwD2XyU7FHgQ4GiO56nXU9GNwOKkPgHjJs2/Nmzg4eB/ziYiYTVgiWjGhAqEyE1Tl9Tx/ravJJtcWfHZHhMlxcxhljpazLUOuSL6ost1mRBqtZMvG5Q/CtAmXqkHpZH3l8uiQMr111bdefPBt8D7K+Zf3nF7ivjPXVpNxK3UM8DUocYdlv2X51rVJCZ8q8h6W+dz1XQo39MwYM9x7Hn4Q9O0/OuXjGuXYWfvKcXQ0WO+WeCWfaa15LneD6iEx76jviTFd4tnu/bT977a+AbUz1s/AuqyOuUy8+DnLLta+Ijg5JUX0/c95m5MsVc00RTlmg/uvXL0tSuWvUuexeOKKpU++2fZ4HzrfJ+oBlj2arZP5RNe8r0qSqPzuuCJeOH7hC2GbrJM4rm3cUWd8685P/K+LOEYzKZq/2aQ8ZmGGIp8HDAReXMs57AssV5ntwDDPWGjMONZyj4RyGFYJqMECcvfHwnXHGzulZciA4OBwMusD30Wyb9iuVD8oBLJMSmKGP7wZS60b5t3yeg9Szgr8OVIC78kjbGe1y33ns8Sfkq2yhQKUzujpFkRmdct0Nj4ww6BAxezdWuN0OcbGXGFLyrsz5l5eXL5lAURblVqRSJddZ89vN4T0EkJvj6h45m71nXjj9twdDPA1+FCY4vS1mvAd7sOqkzTwbDK7FEHWD7wZFoPA8r4ghLBf/gxDptF1lPKyQDlfiFmufH5liKAInyL0qED8qF8uxgu49XMneyeCy87JyFcm4IgMStl1kpJNbK0jyqrYr0tLJGPOqIp86feR77khIlTHp7mOV8bQiXwU3ZxUp2W3rUuKJJ0vG7lcDfVSGo/jODteewO7K9HZX/sx5cUU9jCxd9qz6HgXdfu0lESrnbKX+VeZ/cDu43ZSVnapHwVVz6wxdfKQ9hTNkuGr3/CiUs9otf7U8Z9Xjsou5HJe5xXpcDaA6cFnUZ8h3dT3PpAOfBRx/RBbNyi7/GWvDBW8qA8XJ4zJIVmSI/y6TJPteZR11dQ0e6/p/7rpKP3TIAyZkVL1uvDOdlT0ulc2Hzphk958JjOz9QUiYMBHk+pGN6eqYqf66ec5k0coarmTtlFUEaDZHFRyJmrXXnRNdYN3dseu2cSrx1FF493baBus4orCvbn8V93DaVnZenIF6VijjdRW6DtHgcXH1HFlFtvvqcC/59xCqK7t1R3Grts5c51UQuUp2nCnTmW25AEK1WwUVe9F5tCFz4FVguKcelmlPPWNrHh88XzB7IjKWKp2qiAfOfMLsJw6m1WcMnlkmLlORPsqeOgIFy8c1+AJ2db3rE753SWWeuKwUrq+CW2dqvPk6tu1dIgyvY9/+/f1dkipObqyLr8P5p8g8dY+V/DwHmByNeqK9IKD4njnyCdvisalIyCq23EOssGzVPVW2A9cdHlefs5itIl/duaoNdc8dWeXI0VUbpcjvvZhH7QaDwWAwGAwGd8XqRuVgsBcdMkXNv4x4XT2u2nAblYo86GZGcD3doFHJ5uRyZI5qa++6zgjjTrIDB82c1cH/sbwjt1Vd+N2Nd4dgcNdk5bm/HeIFoQhXHt8r9fJZdVcEIN9b/qzKq7Xprq+Io6x+BJOTOK+6cmftHsHe+oZ4GgwGg8FgMBg8DIZ0GlwJzuwIqMA9y2BQ5EqVfVdlXWTokE5XBZic1ZLJiNkz3WyiI/KtZGJ0x5vJKP4Fvaq9inTCzx2ZVIaUI9ZctpOTr0tcVJk2Z4Lnzco9q767dbnan8647YHKdnOZfI4gdjqhs4avtL9DPA0GT4hbO+UTBJyDGcfBLVClpUeZW6KSZ9bGc6J73zqObifb4wyZuvXNnP2+UEEZB+oquM+yWBSYuKoyKqosliyrCq+v1tNe/b86510QzLIeXUuRqcNBdSeDRZ37+PiwL4h/eXnZfv/+/en7CqpHsVAOvq4zRzKCopN957Jk1PkOOuVdmTPmaUY+MRFVEXWdNpF0dbJUWVjx393zas1kc3sPgafqnoynwWAwGAwG27bNe2YGz4krMyMGAwV+dKXKYsiIDZWloYLZlUdgOpsI+Pf29valXSXnXjhSi8fPPbZ1BFWmFwfWHaIlyjniMRCEzu/fvw+RdwqdzaIKK3Mq2szq4OPfTRfvIW6qa+M+IhFatZWRX1yO/5zcilzbQxxWY7RnTgzxNBg8Ic7aZX60tr4zvpvRHgxWMPP/e2JvgFPVle1SH5WniyM79XswNvR2OCPQ37avj3mpIFI9NoPXK9KD3+uygrP6tgomnFymV9bXDJhFkpF/ezNBVNkquyi+O1Jg5T5U9w2JPG6jS07G51uh2/8s0+goqkc9u4/0dedZldno4Aio0BFBfPJL7DOoeXiERGQdt+deDfE0GAwGg8E3wtFU6sHgnniWrKdHlWtQQ5ED+FjYnoCZjys9rII1DjBVvVmQ645fNT+rwDz60XnEsFMvl1GZJCrA7mSfZZkhv379+pu9EseZ7MmyUDKCzcmSkSRqznbnBd8PN34Z1GOTV4DbWG2Pr+d1tTIn4hqsi9tSOLr+lG7iRyjdZgzOoyrzMUOmW45giKfBYDAYDAaDwcNhyJ3BFagCRhWAxmeXbZMF5dkjYlk9WdBYBbcqk+ooeCwykuTsDBt+pJDBbR8lSbKMLfycZUAp+dzcUsA+qHdOqXYUmeKIJ1Wfwq1IJ9Vu9r2Ckjlbsyt97JBYe6DugSIdGXsyix26dV2a8TTG/yuedUyUkjqrrsE6rhzDMxXMI2HPmK3uZK629YzjODiOe2dnXD2vb11XhVs+anVm3bfUxXva2jOP7m3/VSC3ct1R3OuRpsFxVIQO48ygDuuryI3KvnQzO7pwGVcuA8WRcPhdla/GM8sWOzOgj3qz7CNHcqnsI/7L2sr0R/feqfvksp0c8dTNesrudYcIXEE1dlWb6t7F967e7t4bN1cqUrjT3p74ozPWR3QDfn8I4mmF1T+Kqx3QrnJ9ZDgjsqeOwTEcXQv32G04Ex0ldWR3rrNGO0a6umZwLW41x89s58xAdk87zkm7p0zfBVn/zw5G741MP8ZjJ1Gus+t6q7V81mbaGbvp98ajyfPIUAF3JysEUWXDbNvndxNl9UQ5R+Co4xhMhywqEwOvcX1jEqxDPrmySn5Vjyrj2mHSwPVTEVRcFscO1zFe9/b2JmX6+Pj4+54dvGcd/ZgRWfy5IoSyucoElxqXiiRT7eC44bmsP1yX8tXDvqj7hG2zLNwnvmdqfbItc6jOVSTwtv3LUuuSphlh2SFt3VzAz6+vr7Zt/sz1dWSoMI/aDQaDwWAwGAwOA4ODRyRmBoNbo0u8dMhcVZ8jpFyZvYkEeGyVcIhr9mZJrLSTtb9ynZPzSB9WrssSByryoepnZ5NX3etH1+cdkheRzWO37twGTXdsmDxT5x2hegYU8dYhOc/a4BniyeDRF9cKnmW3daAxjvxgMBgMHgGrWaljswbfGR1Ch4/jr5PhMYTKZuEsHdd+RUypY1mWhDqWreksY4OD6FVdoq7pkig8dhVUBssR8qw7fop4qDLCqva27etc6WQ6cVs4hveMLdmuqAwdl+VWwfXPrRk3TlhGtYH/Oc7LrjkKzgTrZH2p8V4l+QJDPH1zdNjUwePAKbaOUro3zpDrHn07q81HvS+DY+jsHH5nfPf+DTz4MQd1DuE2SFzQNDgfs17vg1U7kZEn7tEoDogz0qsjjwvcFVzWBQax6uXXXC47v/LYE44Bj0fWhw75dEXgX117dVzG8QOPW5Vhw3PwXvGIuscojyJDkGTrznNsiz9jOfcoWlX3I8D1lZGNwx7yaYgngWdKK9yL79qv74bJdroWM6aDwWCQo5sp0LFXfPzRN1TOQmVnvnv/nxVn+gdZRlRGzmaPA50lS5XhhN87QXv3uio7hM+tZu3wtVU/sYy67oz40Ok/HouKsM9kyeZMlQWE2SxuLFazx66Amzduo0StMybUXBtcv7o+k6Gzdiod0Jlv3Yyubtms7opsdhjiCdBduM+C2WX8HlC7C492P5/Zab5lptaj3bdBjmee14PBmeik5cf5zLH9CRt7jFv3c/TWOejct6MZICu+gwua9z7ZwCQH96UrF8uwEjRXOsLJGseqbCenbzpkFx9T97o7R6rPeCwj27r1KCjSSdXfJZTume3kMvYqO6UIPp6fPJerbKfuHFLXKwKvQzqejT0k4t77P8TT1mP6nx1KQd6brR70oZTsd5ujg8FgMHgcuOBshXzq4Cf4IrfKePru43hrrBILCBWw7vXbVLZB1v4KGZZlJ2L9nSBZQWV77A2sM9Ip68uq/1yRT6uydwixlWyn1XMq40eNn9L1LM+9dUwWD/G84D91PZO4bp5G2W37v0fMXBbTCgnqiMZVMmsFHbL3SuJxiCcBnhz3XmRXYEiLwWAwGAwGCt/R7xkMzsae7KBuJksgI5Gq8uqcQ4eA6mTjqMC7yvJYgSOdrkCWTbY3jsoyoBz5VNXjrlHHumPnsrw6194KncwnJtwY2bpUJKDKdKpI3mquVPf77Jhd9TH6VI3XUZxOPHUV6VVtnVXvoy6ys/Dd+qPQMdB76nHo7BR06shSRF1bV+NW82XPWHfG6yjOGu+fsO6OoDPHf9q9OOKcf0fsdeyuwncdZ8Sz9fHK3dqqnm4G/azr74E9meidTJPudSsyZQTWKpGylyBRfd7jw6341tX6y0i6DjmzF05XrIxtVg/CkU8Kbp4diYuzuXcWXDZXZx657CRFviniia9zbbv7osoeISEVunMACahOnau+V5t42tMpV09V156XVZ2N1cX+iM7AZDVp3PK+7XFKM/Z+FWfMgUec2wrPIuegDzb023b7zYBbEV1sG3/ifL51/8dG7sMjj9st183KDvXVQddgDfhrTM6mYOCnYpfu5iZmXZxx/6ssFOzbnkdoAvyC6bge+/H29lbWzTGdC+DVOOE94DowOHZBfiY7HuesF+4bl+1ksOyFy4jq4NevX9uvX792+0l4nRoTLrdad1Wv05tqTFw2F8+nKKt0sJszMWfdvY52fv/+/amO9/f3L/P4/f19e3l5+Xtv3Lg48BpQ/VJkUfa5IntDzq7eWpmn86jdYDAYDAaDweAm2LNL+h0wpNP3Q3dTfmW+V0GyCpazgJ6ze/aQEdV11bkseF+RQfXRkVOPjqv04Fmbc6uZV7cA33+XzbdCllTZS7x+uiSWwtF7zv2q1qaaC50Nkz3Zdt1+3YR4+qlOxmAwGAwGg8HAY9V5f0b8hD4+I6og7IzYxWUocZlMRg44s0z5qm7M6FiFCnzxf6f9aiwq0iDLftqztpjEWiHD9oD7f4v4eC95hFlKj6K3umslW2cuixHLMfHG2UWrcOPYHVeXCYjHXEaV+h/Ym2FXEXcON8t4ekTmdDB4ZnTY/Edba48mz+B5cNYu3uA58Ij66zvhXmO76nx/t3kwBNRjwAWlK6TQalaACmSrNtR8Uddndahz3UwtbK+Sy2VW8ecoqwipDhHI43ZkHblMrLPXZtb/bsbWUZkysm+l3exRueraI3BzGG1KJ9upQx6pR9fcXFkBrpUVsqbKKIzz7hE5R1oFVAZZBy4LMcPNH7Wb7KfB4Dg662jW2uDZkDnf2eMDg+8B5ejNff6+6AZcz27HFNk28/oxkZFM+L87d7FslHekkwv6Vh956ciOAWrVztGMF5fh0/VjES7IXZHtiD45qouuznY6S6+48XUZQ/faGNxz35ksUf3Aa5z/2d1AqcicM+YUf65IJyfHXnJyJevpLu94GoM7GBzHs2U8rTDog4FzfFZ2qgfPB5cdMDgP99bFave/2ll+dmQB/gqJMTgHnfmWZQlhhsVKWyrr4AwCQmXQuPpd5kama48SCxn5lLWncOU6UfOiIl26dVbZTmfZuWo+deRWZbDfVf+vsNk4R3G8qow7lJ37Ed/VdWqz4Oh9Cp3B63J1Trvy8SLzjGzi/q7OZ8bqeJxKPO1lygaDwfmYjKfBs+GeO5GDxwI7gNs2vsWtcbUNqTIsEN+FgJysp8dDRsw48mk1CHX3fW82kyOPVJ2K5OiQT279Y1Abdbk1q+p3dVfkljvfJQCVbNl9ccRE/F/VjVn/z94MqEiNPXq9SzydQdC4erPjGZmryCY1Po4UzebYKgHJ163YWZQ5y3TKZHX6bc/92nPNJRlP38VBGAwGg8FgcBuoXcrxJQbfkVSeef2YyDI9HKp7ydkHLms3kyeu+fXrl82YqwgjrK8bcB7JnnEB/NGxdO3uWVPOxuA9zzJnVpGReR1Zz0CHNMuIi1+/frX7cHYml8oWUkQoy6AIF76PHSJHkUcd4igjVTv3lWVFwhfrfHt7s0RpdkzJWmHvxuCpxNOzGtLvKvdeFnYvnsU5dEb7Hm0/AzJD3zl/pUyDwT2QpS6fWe9Pm+e37v9ZNvQs7A0+rpqPHZluMUa3yhJaHccsqKqC+cHjo8rg2bZcZ6mMnr33XgWOKiDuBLiqjMq+qOrKNglcNtNRfbGaTdMlulTWTJYp5ILtLCsrrtvTB7y2g4xQw/+rUCSOurcdObtlVHbPSv+ydVzNjWzNdknZ1fud1acyntx6df1G0oqRrfmzcJd3PA1q7GWjr6rnGdFRrM/S/0ci9VbZbb7uKmTGpatMV3cDB8+L1dTmM5yGDI8yz47ugrn6sl3FbhsrO4MVVpzeR8Kj6aYzx+iWa6Qzjh15zvIzjmQhDK5BFmRj9gV+R3TmR2SPdOeRqvPj42N7f3//InuGt7c3eRyDUgxOFanEP93uiLmOXkf5s8CYZeF6mIzLMlIU6YSfo388D7AMj3slr/NFMYtoL9HB9VfEqZrfca3r29vb26e6f//+LcclIyEdHFnpCBImUhwqUlERMiubQJXPxOuEy7AecfX9/v07XY/u/joZO2SYIrpU/SsY4mkwGAwGg8FgMBgMCI44yEj0bfObYXuzf1zw584puTLSZs8miyNw92xMMtl1RpYIynN08yQjqs6Qz31faSObh44MWh1vlWmj/lRbj4CzCXxFQJ+RHKIyvVyZigRTMrt6K2L3KIZ4GgwGg8FgcBq6qeyDfZjMl3Mw4zjYtvUsTw4uXSaAyuBwunFPoIpBZ7z7KSOfVLDJGU4uG8TJge+cUtkiVTahyzxxZMhKJsoZ69tlA2XZOJ16FFydR4knN+/2EqCI9/f3U4inI33eg6z+o+ON59QYZ+vL3Ruui9fI6j3sEJ1VnZPxNPiCFeb6Vu1NEDIYDAbfD2eQTWNDamS7noM+HnEc5x7eHpne6hBPLgPJfXfo6j73yFhFYrlHwj4+PuSjQJwB5fqVtbsyNigDZ5DE585jbVw317VyfUY6rWS2ZI9aYd3VZ/W9qrPKqFF1dzOgFAmnxqUr8630X9XOETmy/mdriNcRj73LUOPzMdcyYjQjohWY5MLjQzwNBoNPqJTCOLq3xU8PmgffE9k7BrZt9MwtsKJb7qGHnkX33UvOKkgcnIuMBHFZBhxgx2dVt8veOQKVQVFlr3TIETX3MPh1QbDqI16v5KoIrGwddInhLoGozlVE2irp5GRycnQIg5W2+P7xo1lYRrXhxlyRFnvGRdV3K6g5fSs5HOmUoUNgcfmzNlPcmhriafAF4/D/XIwTOxgMbgHl3Mbxwbk4K4i9JR5NVreTfG9Mxt99cEa2JiKC74xs6ZJY7rozsZoBwvq+ImRU/zNyCdu8gqxz4PF1hMvZpNO9UGU2OeKRiaozMohc+88Apz8qwnOF1Fu9tqtT9mJvttO2DfE0GHxrTMbT4+CIoh4MHh1Ol8ycvwYu9X3PrvgZchxt7162KHtsYfB9oe5xtZZU5oiDI+M5mFcE1FG47KFu/VUwjP2Kd/xEGUekVePdkWd1XfKYZtlUSqYzdVJX/zmy54x2XZaTkmU1Q4vbXJXdrbUrdXH45Efa6F6vyjhSj8uoOtQ1KxmBTB525r3rwyqGeBoMBoPBYHAannn38lkx43sOZhx/JlSA7oioblCM5VWdVWZDVp96PEqRXKqPnJHl2lB1c31vb29L/XEyKRlUPStklRqXLpnBmVJXE9N75ldW18p3hWh/lUTaI/utNx26JGgnA7Hz/q5sLLoZT+5Rt0rOShd04erZgyGeBoPBYDAYDAaDwY9GFpSq4K8TVJ4ZtHXaZRzJNurIsoqzCBUHJ9NeMocJqDMzkhAqA+VIG2eQDR3yZdvu+76kvVAZT3vu65FsXkf0qs+OfNqTbZ5lO10xtxFt4unRJ9B3w4z3cdx6DLspjle0k6XMno1nmZt75LzyuehnGbd7gn+1xu08I/aOa2eXiI8dSfl/Fpwt65VrKmvvUes7E1c9FvIM9d2rnUeeD4M+ugG1Ct4VEaHq5sfp+JqjtkwF+xnZgJ8xU4nri/8umwPttJMZr/3169fffuOvbn18fGxvb2/b+/v79vv377Rfrk/umirYr2y4egQKs4DievcrYipjivU1/mG/OvOi27/Mh3Jj0MnuUploqu9cR9cfqOZANm5VdmHn3mOf+D6pOmKOR1vv7+9/H0N1ZBLW0fnFRjcPqqzEWHc8Z7kOrOfKmPIuGU9H2MF74Z67A4NBF1elAJ/V5llrf289Z5EWg/uCd2tudR/3pGMPvj+u2lC4qq2z8YgynYErib2r8F3vxa3ANoWD+JWsmazsy8tLK9jM6sa6sqBYBeK/fv2SAbE6xv3vyufsZXacCYVqDFlmPOfkZRLIya/IlayeqnyX8FLlHXnRQWcDL5MBj7nNt6v1YyeLyhFuahw7pJtaU9WGpSKpVH0s714oclu14Yju7NqruJp51G4wGAwGT4crHZ0zHILBYPA98Cyk06APR9woEues++/Ig5X6XaYJb8bg+SwYXwlarxgHPsZZM9y/FXky4mol40YdY3Kyc20m89X+hiNOuvO9k7VzVL6KuOvWU9WtiEdeM5k+cFCZQ9ncONK/lbXpyu7RQUcI0MAQT4PB4Ga40nEZ/BxUztNZbdwym2owGBzH1QHy6ITvh24Wxcp1rh4OSh15gciyLRQ5E59Vhgs+btNpO8uYiPqOEGcokwrYs/Y72UPRzw45tIIsiFff1TitEC3dcVakY5VhhtdVc8HJ3kU1l7juI+SMIpNc1pebW5Uf6AhT/rVH7pf67mRx8qpx6pDIqySo0ztDPA0Gg8HgxyJLed+LLOgYDAbn4oy11lmzR/VDJ0gbPCeyYIzJnZU6OdCrArczSJ6szm5gndXDa60Kzjv1rZJEnAnFx/dizzgf7T9+d2O7Sj5lqMZ15T6skmh8nZtTR+HILEdOsVxclxuD9/f3T8QO/6k6FfEX5TJSbBVHMpw6da1iiKfBYHATdBXVWUHDEAbfE2c6l6vtOcxcGwy+FzqB1GQ/PT9WslCO3m+XHZEhC4ar7I0OqdQ5H/8VQcJZFE4+J/8qkdchJjqPQ+0Fk49q/vB9fnn5+j4vJj7U/1U5OcOJ71mHAFHf3Wcn44rsal6doVNdxh+OARM/nXXissfif0Y8uesy0jSzO6ubrdn9d22tkOVdDPE0GAwGg6eE2xlaIYt+cuB4dWbIYNCFeyTkKqzObaVrzl4fQ2DfDp3AWuGM7IPVbKfssaRKLg60q+uzzBBHPrmsjoygWSWpVN0qW0TVF8TPGRkfeG0W+Ku2qoA/I50UeaXgsnmyPqisI1eeP7s2Kv+qO3+xzc7aq+wIkjYV8bNC7DD5xPOturaTIZbZn44/mxHRK75gtu46GOJpMBgMBk+PvUZwshYGg8EKhiD6OTgrw7aTAZEhC9S7QSO2+/v37y/Htu3zu58w4I2fY68y/yrSwmE164Uzj5xs0Z9Hs/Od4H01o0Vdu7LB1iV39pZ3ssXnLrmzpw2eA2r+YDvVPOe64z+v8UeZc6troHtf99z/IZ4Gg8FgMBgMBoPBj0U38D2LeOxkV6ngfJV0csSTyqpR9fOfkzXLelJycR8dSbAKR2xcneVcZZt0kGU9Vdd1Mp2UXBnpkmVyXYFbEoSdR90Yao1wnY9GOlWo9NpZj9gFHpJ4WmW+b4FHkOGn4CxG9hHnEaKbgrp6fjD4buju1q2UHzznOD2azEofP5qMCkeDuivqrtrpBiVnPEZzVf23qHd8hHV0SKfVR1TinHq/T0YgucyoyDxS1zg95MigFZm6a16RFBWR4R4fCyIF6+VjKmBmcsw9ItS9l25eZOQCy/D29ibP4We8r1x/J/B39wrrcWPtyKXOfY86HTm5Sp59fHy0f3FRgTOaVF86ZJq7T3xN9F+Nb0V0Yv14LWbwVcQt9+1IBhp+z9bCHoIT0Saebm3IVArcMzhz3xVHUj7vibN2UB4RZ6zJW+3cnYlHdaofVa5nwpXpvYPBHnTm2k/xUSqH88r+VzvNqzKs3NdbtPXd586jQREYrlyGvWTNnjUUwa0KFPHzUVJZBZiVvB1ipMoU4TadfNzHbpZORRZxu6vxZ4eErAgN7Nse/dIhWo7A1ctESVdfq/oxXsvaU3D3di+Blc2LLK5cITE7UPNwlQhX162QSEzkXU48DQaDwWDw0zAk12Bwe1TB5GBwBGeQTkfLu2uzgJaziFRmhNpwzciqTJZO5kVFvrl+uPocGVONizp3RqB+FCpLjUmaQDUfu+OmxgHHsYMueYpzxM3DW8L1feU6JXtGhqrzipxS87Eim1GW1fmdoZqLK3WsYIinwWAwGAwGg8FdwbvMKmAYAmpwK6wEVVUAulLP3qylI+268iooRdnc40WqfEYMrTzexTI42fi7u6b6vkrcMdS1nYwRNV6dubE6lkegSCeXNZaBCZ492VJcT4YVUtTNgywDiDO/9sjI5TPyagVOVx3JYlrBEE+DwWAwGAh0DPEEwoPBcfDObidoHQyugNv9d/Ovk/WQXcNtdDJFHLnAwXAX3YyHLAhGwqAirFbkWh2T7DPW67535e2Ob4e0Uo+tdYl3le11hLxZwZn3mutF7CGw2Has+HLK7lQ2yK2huA7fB7WKM21fhwjt2tvJeBoMBoPB4ERMsDsY3AaOfIrv+H8wOAOKqOFHULoZCxkBxcdVkLcnuyXkUQG/+6xIig5wDWYESGccuD5XF9bRLb+CDgHFbR7NDMkeGcTzmbzZo3RY9p6PuZ3Z9q9fv+zL0bvIxpXn9F4CF6/ndpEAq7LvbgHWc1dmmCGGeBoMBoPBQOAWO4aDwaCHbhA+GFToPG6igjFFjnYyWvB4Rc5UxERVloNeR1Lg90xu7rMjnrJ+qzq4jaqulbVdZaqo+jNbf7UfUN2D1UyirL5b+DRn6OHuuurUkc2nish0fuDVtqbKgutmAio40umsjLUMQzwNBoPBYDAYDO4O95jdYHA2FIHkSKhuIOYes8nKr17DwABakU57MoRcppfLzMLAdSX7ZjXAdWSBepzKtVmRMVcH36vE3Uq9Z9W1t93sWAZHMq32o5Op1MnCq+rELMVsfalMKm7LZVY62RWhuqIzKgL86ky5IZ4Gg8FgMDC46jn3wWCgMZmGg6vhSKcsE2kvCeWC4SNzXAWw8Rl/RS3L6sjqxoCZg+yVx86OZinyfdpDaBzVJy6jrFtnJbPLYlkl8VSbZ2QNVe0oAuQo8XQEWZ9VdiH+j8+8vlz2U0aE7pHTjVus6yoTaw9Bvlcn7SWnHpZ44knwk9C58bcek7PaO1PuowbtUbHKep/Z1t5dhlti787jvfGMc/FRoHZxM6DjHdfg/7Mx97aGC5S+A67szx7H8laPhdwKjzBfjuqQR7NHPx3hQ4WtUFkAWXZBIMuuiHfSuMdZsI7VYF1lOcX/9/f3XfOU57gj2jI/0a2TLKZz5BWPiSN/WB4mylSAz33DuvFdQtn9zciwrE+qf474xGOvr68t37+ra6r72M3Mqu7T+/t7ml2T1Z+1r8oFOVPJjMfxPlZzEecNzx1VZtu+/vKjkifLNorvb29vn+QL3YXt7o2LmTBUZfh8NWYObeLpTDKkqusRSadbOw0rYzS4H87asQp07+vRHZx74NnkRTyz7M+MzMHoXssGcgWjZ8+D2qW7anzvvV6PZgBwHVePV1eOK6/56TgzoBz04MiLlbVXlWXiqrrPTERkbfM1GfYQ2CsBfLeeLDNkpW6+XyubpnyPsywT/HxUFzrSSclQkZRZexnJotqqzmV1sWzZMSYpKlmz8c+Im0we1cbKfc2ISFWmu4Y646mIV/y+SlwftSd763jYjKfBYDAYDAaDwWAwuBpVBs9e4vdqwngvebPnmntuFriMmaPEk8M9N55WMlg69cRnPL5tOVnaJW6q712iheVW/1UbeE2WcbeHfD2CK+qv7uce8NpRJGiXLO9giKcHxey0DwaDwVfMrv9gMBgMzoDLnECsZBQxUaUeTVFtrQbnZ2UOqfIoZ5Z1sdIWP0q0h4zgeuK/CpyzulRWmyIW98q4Fyz3ahzoZOtkdK2cc20eyZCrCBVeR1F3tw+djMMVos8Rbpn+OGvunMEPZIQtr6NsXu7JehriaTAYDAZPg1sST6vp57fAo+/UDgaDwaOj89hON+ukyqRQJI4KVs8iOioy7axH6KKuo0QXP4LVvZ6D55X2FXmhyIlbb3Rlj2llWXcrGV9Rl+qny6JRY65kdO27bDUlE7atyNtO39Sx1ay6DpiYybLFlAx75le2fs+ar9yfjg7p+p1DPD0gJmgYDAaDx8EZKe+DwWAweEwoAiPLzuFy29bPLFFkB3/GelVQu0qIMdHSfUznFpsvXfJIZWgwIaJIimxMEfcmnZQs7hxihXRw2SpVtlV3Haxk9Dn5UCZFPMXfr1+/lrK89s7hjCBT5brk05H55cZ5D5ye4fnQIZ86GOJpMBgMBk+FytBe4SQP+TQYDAbfA1VmhiJq1K9TZcgyVLBMRu5kgXWXGOO6svKdR4a6x7ndPceqep2cWfDfqTf+39rmZ2O8R5aMgMmynCoyUs2lipBx35V8inSKv/g1t/f3d/sLxlX9mSwrBB6OBY9jZz2vzrHufenA3ccOmcckb3YdY4inwWAwGDwN7rkLecQRPYss25tqPhgMBoM+WNeqn2rfS8a48lkAqLKjVBZC9/GnCi77p5sttdL+WXZ9NdPFla/6fQusEkKd69X5TqZOgOfn6pjsfbRM9SVbA/gf260yuc7IHnJZZWdhhaA+AzjO6v8qhngaDAaDwVPhlhlPKn18iJ3BYDD4HnAZGSuPB+G1R+wDPk6k5OsEfFkwrh6rcY9bXWVn3fjuJSX4Ws6S4fLZI0MuG+ZIoH0Uq4TQ2W0dqXs1CyfaVllPeD57rO9eUKTTWVjJnDwTZ5NO23Yy8bRncqnPj4izdhA6dQ+eD/c0Rtu2z0HKzq/WfeX62AvX1yPOzeBaKAeY04FXHhPoGshq/t6bbMqCkHvJ8RNwll47ep/Ovs97ds2PzsHssQ5Vrgqcj6C7Y3xV4HDlOhpbdQ54vocNcPN3JQvhFnokZOWsEM4WqepwNjkjQ3gNq7Gs+td9HMv1pdIX8djk6j3N0CHt1Bi6tve0z/dlVT/j/Vbyvb+/y3vo2una0Gy9KQKqQ8J15lk2zmrNZP1gOd0aw3Wp5vzeGAUfBXZrz13LvnImwxE5t22BeOo2UJVTz0gfZc0fISBYkeER5F7FLTMMHhHPEnB1FO0qjjLpjzzf95BqV7f3k6EIH/5TZdmRqALj7py89/3qOg+3In6VHLdcH7fY8e3gKvKv04d7zcmzd1SzQOWe666a12fee5VRMbg9eOydrakIl237F+PgNRwQquC+2lRheR0UIXBk4ySuV/Oe+4LHXH/UNd1g15EEe/rGsegqqeGOZUG58m9YBkWyZFBzdQ/ppO7x+/u7Jce6+kpdvzJGeHzlHWuO4FMkLJ5TRBDPz+o+ZucUGVxdp/STusdMeGU6TUGNU0YGX048DQaDwWAwGAwGg8F3hCP5HfGCcJseqwHgd4Uamy4BvUrIVKiyjVje74hsYw/LbNtjbBzvJVD3XqfmKI7HI43NFcjWyBEM8TQYDAaDp0Jnp+i7OgODwU/GrOvBraCyDCJAVySIywjALChV50+f0+4xH5UVcgXxlGXlfEdg5g6TTjz21aNit0aW8e0yFePzityu35wB+J3nCaKbEdjBEE+DwWAweEqolOKf7sQPBoPBoA8OIlVgiYRTVQ8Df/LdPZ628hjRs8GRAYgO2XNVtpM6/hPIp5eXl+3Xr1/pvH90qMwj91jamW1yO9ieKvvMOPsVBy8f32FUBoPBYDAYDAaDwWAwGAwGD4dfdZHBYDAYDAaDwWAwGAwGg8FgHUM8DQaDwWAwGAwGg8FgMBgMLsEQT4PBYDAYDAaDwWAwGAwGg0swxNNgMBgMBoPBYDAYDAaDweASDPE0GAwGg8FgMBgMBoPBYDC4BEM8DQaDwWAwGAwGg8FgMBgMLsEQT4PBYDAYDAaDwWAwGAwGg0swxNNgMBgMBoPBYDAYDAaDweASDPE0GAwGg8FgMBgMBoPBYDC4BP8fTjFPEaSlJ5EAAAAASUVORK5CYII=">
</fieldset>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 12 cell-12-cell_type" class="cell markdown" data-index="12" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-markdown-label nb-cell-label cell-12-loc nb-loc-label" aria-labelledby="nb-cell-label 12" class="nb-anchor" href="#12" id="12">
12
</a>
</td>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-12-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-12-select">
cell type
<select form="cell-12" name="cell_type">
<option id="cell-12-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-12-source-label" class="nb-toolbar" hidden id="cell-12" name="cell-12">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-12-start" role="none">
</td>
<td class="nb-end" hidden id="cell-12-end" role="none">
</td>
<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-12-source-label nb-source-label" class="nb-source" form="cell-12" id="cell-12-source" name="source" readonly rows="1">Next, we combine the three `.jpg` cutouts into a single, colorized image:</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>Next, we combine the three <span class="sb" title="Literal.String.Backtick">`.jpg`</span> cutouts into a single, colorized image:
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-12-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-12" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-12-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-12-loc" role="none">
1
</td>
<td class="nb-outputs" role="none">
<p>
Next, we combine the three
<code>
.jpg
</code>
cutouts into a single, colorized image:
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 13 cell-13-cell_type" class="cell code" data-index="13" data-loc="1" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-code-label nb-cell-label cell-13-loc nb-loc-label" aria-labelledby="nb-cell-label 13" class="nb-anchor" href="#13" id="13">
13
</a>
</td>
<td class="nb-execution_count" role="none">
<label class="nb-execution_count" id="cell-13-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
6
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-13-select">
cell type
<select form="cell-13" name="cell_type">
<option value="markdown">
markdown
</option>
<option id="cell-13-cell_type" selected value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-13-source-label" class="nb-toolbar" hidden id="cell-13" name="cell-13">
<fieldset>
<legend>
actions
</legend>
<button type="submit">
Run Cell
</button>
</fieldset>
</form>
</td>
<td class="nb-start" hidden id="cell-13-start" role="none">
<time datetime="2024-02-02T04:06:36.843+00:00">
2024-02-02T04:06:36.843175+00:00
</time>
</td>
<td class="nb-end" hidden id="cell-13-end" role="none">
<time datetime="2024-02-02T04:06:36.856+00:00">
2024-02-02T04:06:36.856445+00:00
</time>
</td>
<td class="nb-source" role="none">
<textarea aria-labelledby="cell-13-source-label nb-source-label" class="nb-source" form="cell-13" id="cell-13-source" name="source" readonly rows="1">Image.merge("RGB", [Image.open(red), Image.open(green), Image.open(blue)])</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="n" title="Name">Image</span><span class="o" title="Operator">.</span><span class="n" title="Name">merge</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"RGB"</span><span class="p" title="Punctuation">,</span> <span class="p" title="Punctuation">[</span><span class="n" title="Name">Image</span><span class="o" title="Operator">.</span><span class="n" title="Name">open</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">red</span><span class="p" title="Punctuation">),</span> <span class="n" title="Name">Image</span><span class="o" title="Operator">.</span><span class="n" title="Name">open</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">green</span><span class="p" title="Punctuation">),</span> <span class="n" title="Name">Image</span><span class="o" title="Operator">.</span><span class="n" title="Name">open</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">blue</span><span class="p" title="Punctuation">)])</span>
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-13-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-13" name="metadata" onclick="openDialog()">
metadata
</button>
<dialog id="cell-13-metadata">
<form>
<button formmethod="dialog">
Close
</button>
<pre><code>
{'execution': {'iopub.status.busy': '2024-02-02T04:06:36.843025Z', 'iopub.execute_input': '2024-02-02T04:06:36.843175Z', 'iopub.status.idle': '2024-02-02T04:06:36.856763Z', 'shell.execute_reply': '2024-02-02T04:06:36.856445Z'}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-13-loc" role="none">
1
</td>
<td class="nb-outputs" role="none">
<fieldset class="nb-outputs" data-outputs="1">
<legend aria-describedby="nb-outputs-desc" id="cell-13-outputs-label">
<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
6
</span>
<span aria-hidden="true">
]
</span>
</legend>
<span class="visually-hidden" id="cell-13-outputs-len">
has 1 output
</span>
<img 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cell type
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<textarea aria-labelledby="cell-14-source-label nb-source-label" class="nb-source" form="cell-14" id="cell-14-source" name="source" readonly rows="1">If instead you want to create and colorize images from FITS files, you can use the built-in functions in `astropy.visualization`. More details are available [here](https://docs.astropy.org/en/stable/visualization/rgb.html).</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>If instead you want to create and colorize images from FITS files, you can use the built-in functions in <span class="sb" title="Literal.String.Backtick">`astropy.visualization`</span>. More details are available [<span class="nt" title="Name.Tag">here</span>](<span class="na" title="Name.Attribute">https://docs.astropy.org/en/stable/visualization/rgb.html</span>).
</code></pre>
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{}
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1
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<p>
If instead you want to create and colorize images from FITS files, you can use the built-in functions in
<code>
astropy.visualization
</code>
. More details are available
<a href="https://docs.astropy.org/en/stable/visualization/rgb.html">
here
</a>
.
</p>
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<textarea aria-labelledby="cell-15-source-label nb-source-label" class="nb-source" form="cell-15" id="cell-15-source" name="source" readonly rows="6">## Getting FITS cutouts
Using a different target, we'll access the cutouts as astropy FITS objects. This time, however, we'll use the `get_cutouts` method, which does not download the file into the working directory. The images are loaded into memory but not saved.
We'll also use MAST `Catalogs` to overlay Gaia sources on our image.
**Note:** The runtime of the below cell varies, depending on the server load at the time of request. It should finish running in under 30 seconds.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Getting FITS cutouts</span>
Using a different target, we'll access the cutouts as astropy FITS objects. This time, however, we'll use the <span class="sb" title="Literal.String.Backtick">`get_cutouts`</span> method, which does not download the file into the working directory. The images are loaded into memory but not saved.
We'll also use MAST <span class="sb" title="Literal.String.Backtick">`Catalogs`</span> to overlay Gaia sources on our image.
<span class="gs" title="Generic.Strong">**Note:**</span> The runtime of the below cell varies, depending on the server load at the time of request. It should finish running in under 30 seconds.
</code></pre>
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<h2>
<a href="#getting-fits-cutouts">
Getting FITS cutouts
</a>
</h2>
<p>
Using a different target, we'll access the cutouts as astropy FITS objects. This time, however, we'll use the
<code>
get_cutouts
</code>
method, which does not download the file into the working directory. The images are loaded into memory but not saved.
</p>
<p>
We'll also use MAST
<code>
Catalogs
</code>
to overlay Gaia sources on our image.
</p>
<p>
<strong>
Note:
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<textarea aria-labelledby="cell-16-source-label nb-source-label" class="nb-source" form="cell-16" id="cell-16-source" name="source" readonly rows="5"># As before, we define our coordinates
coord = SkyCoord(53.22706, -27.90232, unit="deg")
# And request cutouts at that location
cutouts = Zcut.get_cutouts(coord, size=300)</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># As before, we define our coordinates</span>
<span class="n" title="Name">coord</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">SkyCoord</span><span class="p" title="Punctuation">(</span><span class="mf" title="Literal.Number.Float">53.22706</span><span class="p" title="Punctuation">,</span> <span class="o" title="Operator">-</span><span class="mf" title="Literal.Number.Float">27.90232</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">unit</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"deg"</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># And request cutouts at that location</span>
<span class="n" title="Name">cutouts</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Zcut</span><span class="o" title="Operator">.</span><span class="n" title="Name">get_cutouts</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">coord</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">size</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">300</span><span class="p" title="Punctuation">)</span>
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<textarea aria-labelledby="cell-17-source-label nb-source-label" class="nb-source" form="cell-17" id="cell-17-source" name="source" readonly rows="3">Now, we use one of the cutouts to get its [World Coordinate System](https://fits.gsfc.nasa.gov/fits_wcs.html) (WCS) information. When plotting, we can now make sure that each pixel corresponds to its coordinates on the sky. This gives us the ability to overlay images based on coordinates.
Our overlay will be high-precision coordinates from the Gaia mission. We use the the astroquery.mast `Catalogs` class to search the Gaia database.</textarea>
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<pre><span></span><code>Now, we use one of the cutouts to get its [<span class="nt" title="Name.Tag">World Coordinate System</span>](<span class="na" title="Name.Attribute">https://fits.gsfc.nasa.gov/fits_wcs.html</span>) (WCS) information. When plotting, we can now make sure that each pixel corresponds to its coordinates on the sky. This gives us the ability to overlay images based on coordinates.
Our overlay will be high-precision coordinates from the Gaia mission. We use the the astroquery.mast <span class="sb" title="Literal.String.Backtick">`Catalogs`</span> class to search the Gaia database.
</code></pre>
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<p>
Now, we use one of the cutouts to get its
<a href="https://fits.gsfc.nasa.gov/fits_wcs.html">
World Coordinate System
</a>
(WCS) information. When plotting, we can now make sure that each pixel corresponds to its coordinates on the sky. This gives us the ability to overlay images based on coordinates.
</p>
<p>
Our overlay will be high-precision coordinates from the Gaia mission. We use the the astroquery.mast
<code>
Catalogs
</code>
class to search the Gaia database.
</p>
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<textarea aria-labelledby="cell-18-source-label nb-source-label" class="nb-source" form="cell-18" id="cell-18-source" name="source" readonly rows="6"># Pull the WCS/image data from our cutout
cutout_wcs = WCS(cutouts[1][1].header)
cutout_img = cutouts[1][1].data
# Search Gaia for data in the vicinity of our target
sources = Catalogs.query_region(catalog="Gaia", coordinates=coord, radius=.5*u.arcmin)</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Pull the WCS/image data from our cutout</span>
<span class="n" title="Name">cutout_wcs</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">WCS</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cutouts</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">][</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">header</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">cutout_img</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">cutouts</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">][</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">data</span>
<span class="c1" title="Comment.Single"># Search Gaia for data in the vicinity of our target</span>
<span class="n" title="Name">sources</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Catalogs</span><span class="o" title="Operator">.</span><span class="n" title="Name">query_region</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">catalog</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"Gaia"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">coordinates</span><span class="o" title="Operator">=</span><span class="n" title="Name">coord</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">radius</span><span class="o" title="Operator">=</span><span class="mf" title="Literal.Number.Float">.5</span><span class="o" title="Operator">*</span><span class="n" title="Name">u</span><span class="o" title="Operator">.</span><span class="n" title="Name">arcmin</span><span class="p" title="Punctuation">)</span>
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<textarea aria-labelledby="cell-19-source-label nb-source-label" class="nb-source" form="cell-19" id="cell-19-source" name="source" readonly rows="1">Now we can set up our graph to show an overlay of the image and Gaia coordinates (marked with an 'x'). </textarea>
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<pre><span></span><code>Now we can set up our graph to show an overlay of the image and Gaia coordinates (marked with an 'x').
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Now we can set up our graph to show an overlay of the image and Gaia coordinates (marked with an 'x').
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<textarea aria-labelledby="cell-20-source-label nb-source-label" class="nb-source" form="cell-20" id="cell-20-source" name="source" readonly rows="20"># Create the figure on a WCS projection
fig, ax = plt.subplots(subplot_kw={'projection':cutout_wcs})
fig.set_size_inches(10,10)
plt.grid(color='white', ls='solid')
# Setup WCS axes
xcoords = ax.coords[0]
ycoords = ax.coords[1]
xcoords.set_major_formatter('d.ddd')
ycoords.set_major_formatter('d.ddd')
xcoords.set_axislabel("RA (deg)")
ycoords.set_axislabel("Dec (deg)")
ax.imshow(cutout_img, cmap='gray',vmin=0,vmax=1)
ax.plot(sources['ra'],sources['dec'], 'x',ms=20, mew=3, color="#df0040", transform=ax.get_transform('icrs'))
ax.set_xlim(0,cutout_img.shape[1]-1)
ax.set_ylim(cutout_img.shape[0]-1,0)
plt.show()</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Create the figure on a WCS projection</span>
<span class="n" title="Name">fig</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">ax</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">subplots</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">subplot_kw</span><span class="o" title="Operator">=</span><span class="p" title="Punctuation">{</span><span class="s1" title="Literal.String.Single">'projection'</span><span class="p" title="Punctuation">:</span><span class="n" title="Name">cutout_wcs</span><span class="p" title="Punctuation">})</span>
<span class="n" title="Name">fig</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_size_inches</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">10</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">10</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">grid</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">color</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'white'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">ls</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'solid'</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Setup WCS axes</span>
<span class="n" title="Name">xcoords</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">coords</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">ycoords</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">coords</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">xcoords</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_major_formatter</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'d.ddd'</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ycoords</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_major_formatter</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'d.ddd'</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">xcoords</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_axislabel</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"RA (deg)"</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ycoords</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_axislabel</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"Dec (deg)"</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">imshow</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cutout_img</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">cmap</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'gray'</span><span class="p" title="Punctuation">,</span><span class="n" title="Name">vmin</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">,</span><span class="n" title="Name">vmax</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">plot</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">sources</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'ra'</span><span class="p" title="Punctuation">],</span><span class="n" title="Name">sources</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'dec'</span><span class="p" title="Punctuation">],</span> <span class="s1" title="Literal.String.Single">'x'</span><span class="p" title="Punctuation">,</span><span class="n" title="Name">ms</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">20</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">mew</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">3</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">color</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"#df0040"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">transform</span><span class="o" title="Operator">=</span><span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">get_transform</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'icrs'</span><span class="p" title="Punctuation">))</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_xlim</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">,</span><span class="n" title="Name">cutout_img</span><span class="o" title="Operator">.</span><span class="n" title="Name">shape</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">-</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_ylim</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">cutout_img</span><span class="o" title="Operator">.</span><span class="n" title="Name">shape</span><span class="p" title="Punctuation">[</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">-</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">show</span><span class="p" title="Punctuation">()</span>
</code></pre>
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{'execution': {'iopub.status.busy': '2024-02-02T04:08:07.385740Z', 'iopub.execute_input': '2024-02-02T04:08:07.386422Z', 'iopub.status.idle': '2024-02-02T04:08:08.366427Z', 'shell.execute_reply': '2024-02-02T04:08:08.365992Z'}}
</code></pre>
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<textarea aria-labelledby="cell-21-source-label nb-source-label" class="nb-source" form="cell-21" id="cell-21-source" name="source" readonly rows="5">It's interesting to note that Gaia recognises two stars in the brightest spot of the image. At the same time, the far fainter point in the lower right is not in the Gaia catalog at all!
### For further reading
* [Gaia Mission](https://www.esa.int/Science_Exploration/Space_Science/Gaia)
* [3D-HST](https://archive.stsci.edu/prepds/3d-hst/), the deepfield survey we use in the image file example</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>It's interesting to note that Gaia recognises two stars in the brightest spot of the image. At the same time, the far fainter point in the lower right is not in the Gaia catalog at all!
<span class="gu" title="Generic.Subheading">### For further reading</span>
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Gaia Mission</span>](<span class="na" title="Name.Attribute">https://www.esa.int/Science_Exploration/Space_Science/Gaia</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">3D-HST</span>](<span class="na" title="Name.Attribute">https://archive.stsci.edu/prepds/3d-hst/</span>), the deepfield survey we use in the image file example
</code></pre>
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{}
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5
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<p>
It's interesting to note that Gaia recognises two stars in the brightest spot of the image. At the same time, the far fainter point in the lower right is not in the Gaia catalog at all!
</p>
<h3>
<a href="#for-further-reading">
For further reading
</a>
</h3>
<ul>
<li>
<a href="https://www.esa.int/Science_Exploration/Space_Science/Gaia">
Gaia Mission
</a>
</li>
<li>
<a href="https://archive.stsci.edu/prepds/3d-hst/">
3D-HST
</a>
, the deepfield survey we use in the image file example
</li>
</ul>
</td>
</tr>
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<textarea aria-labelledby="cell-22-source-label nb-source-label" class="nb-source" form="cell-22" id="cell-22-source" name="source" readonly rows="10">### About this Notebook
**Authors:** Natalie Korzh, Thomas Dutkiewicz
**Last Updated:** July 2022&lt;br&gt;
**Next Review:** Jan 2023
&lt;img style="float: right;" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" alt="STScI logo" width="200px"/&gt;
[Top of Page](#Beginner:-Zcut-using-Astrocut-and-Astroquery-Tutorial)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">### About this Notebook</span>
<span class="gs" title="Generic.Strong">**Authors:**</span> Natalie Korzh, Thomas Dutkiewicz
<span class="gs" title="Generic.Strong">**Last Updated:**</span> July 2022&lt;br&gt;
<span class="gs" title="Generic.Strong">**Next Review:**</span> Jan 2023
&lt;img style="float: right;" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" alt="STScI logo" width="200px"/&gt;
[<span class="nt" title="Name.Tag">Top of Page</span>](<span class="na" title="Name.Attribute">#Beginner:-Zcut-using-Astrocut-and-Astroquery-Tutorial</span>)
</code></pre>
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{}
</code></pre>
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10
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<td class="nb-outputs" role="none">
<h3>
<a href="#about-this-notebook">
About this Notebook
</a>
</h3>
<p>
<strong>
Authors:
</strong>
Natalie Korzh, Thomas Dutkiewicz
</p>
<p>
<strong>
Last Updated:
</strong>
July 2022
<br>
<strong>
Next Review:
</strong>
Jan 2023
</p>
<img alt="STScI logo" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" style="float: right;" width="200px">
<p>
<a href="#Beginner:-Zcut-using-Astrocut-and-Astroquery-Tutorial">
Top of Page
</a>
</p>
</td>
</tr>
</tbody>
</table>
<table class="nb-cells-footer" hidden>
<tbody>
<tr class="total">
<th scope="row">
all cells
</th>
<th scope="row">
count
</th>
<td class="nb-ordered">
unexecuted
</td>
<td class="nb-cell_type">
22
</td>
<td class="nb-source">
</td>
<td class="nb-outputs">
8
</td>
<td class="nb-toolbar">
</td>
<td class="nb-metadata">
</td>
<td class="nb-loc">
113
</td>
</tr>
<tr class="code">
<th scope="row">
code cells
</th>
<th scope="row">
count
</th>
<td class="nb-ordered">
unexecuted
</td>
<td class="nb-cell_type">
22
</td>
<td class="nb-source">
</td>
<td class="nb-outputs">
8
</td>
<td class="nb-toolbar">
</td>
<td class="nb-metadata">
</td>
<td class="nb-loc">
113
</td>
</tr>
<tr class="markdown">
<th scope="row">
markdown cells
</th>
<th scope="row">
count
</th>
<td class="nb-ordered">
</td>
<td class="nb-cell_type">
22
</td>
<td class="nb-source">
</td>
<td class="nb-outputs">
</td>
<td class="nb-toolbar">
</td>
<td class="nb-metadata">
</td>
<td class="nb-loc">
113
</td>
</tr>
</tbody>
</table>
<dialog id="nb-metadata">
<form method="dialog">
<button formmethod="dialog">
Close
</button>
{"language_info": {"name": "python", "version": "3.12.1", "mimetype": "text/x-python", "codemirror_mode": {"name": "ipython", "version": 3}, "pygments_lexer": "ipython3", "nbconvert_exporter": "python", "file_extension": ".py"}}
</form>
</dialog>
<form aria-labelledby="nb-notebook-label" id="notebook" name="notebook">
<label>
<input id="nb-edit-mode" name="edit" type="checkbox">
edit mode
</label>
<button aria-controls="nb-metadata" onclick="openDialog()">
metadata
</button>
<button type="submit">
Run
</button>
</form>
</main>
<section id="nb-dialogs">
<details>
<summary onclick="openDialogs()">
settings, help, &amp; diagnostics
</summary>
</details>
<dialog id="nb-help">
<h1>
<a href="#notebook-help">
notebook help
</a>
</h1>
<form>
<button formmethod="dialog">
close
</button>
<h2 id="nb-defs-label">
<a href="#nb-defs-label">
definitions
</a>
</h2>
<dl aria-labelledby="nb-defs-label">
<dt id="nb-notebook-label">
notebook
</dt>
<dd>
a collections of
<a href="#nb-cells-label">
cells
</a>
</dd>
<dd>
<dl aria-labelledby="nb-notebook-label nb-defs-label">
<dt id="nb-root-metadata-label">
metadata
</dt>
<dd id="nb-root-metadata-desc">
Notebook root-level metadata.
</dd>
<dd>
<dl>
<dt id="nb-kernelspec-label">
kernelspec
</dt>
<dd>
Kernel information.
</dd>
<dd>
<dl aria-labelledby="nb-kernelspec-label">
<dt>
kernel name
</dt>
<dd>
Name of the kernel specification.
</dd>
<dt>
display name
</dt>
<dd>
Name to display in UI.
</dd>
</dl>
</dd>
<dt id="nb-language_info-label">
language info
</dt>
<dd>
Kernel information.
</dd>
<dd>
<dl aria-labelledby="nb-language_info-label">
<dt>
programming language
</dt>
<dd>
The programming language which this kernel runs.
</dd>
<dt>
<a>
codemirror mode
</a>
</dt>
<dd>
The codemirror mode to use for code in this language.
</dd>
<dt>
file extension
</dt>
<dd>
The file extension for files in this language.
</dd>
<dt>
mimetype
</dt>
<dd>
The mimetype corresponding to files in this language.
</dd>
<dt>
pygments lexer
</dt>
<dd>
The pygments lexer to use for code in this language.
</dd>
</dl>
</dd>
<dt>
title
</dt>
<dd>
The title of the notebook document
</dd>
<dt>
authors
</dt>
<dd>
The author(s) of the notebook document
</dd>
</dl>
</dd>
<dt id="nb-cells-label">
cells
</dt>
<dd id="nb-cells-desc">
Array of cells of the current notebook.
</dd>
</dl>
</dd>
<dt id="nb-cell-label">
cell
</dt>
<dd>
<dl aria-labelledby="nb-cell-label nb-defs-label">
<dt id="nb-index-label">
index
</dt>
<dd id="nb-index-desc">
the ordinal location of the cell in the notebook, counting begins from 1.
</dd>
<dt id="nb-source-label">
source
</dt>
<dd id="nb-source-desc">
Contents of the cell, represented as an array of lines.
</dd>
<dt id="nb-metadata-label">
metadata
</dt>
<dd id="nb-metadata-desc">
Cell-level metadata.
</dd>
<dd>
<dl aria-labelledby="nb-metadata-label">
<dt>
name
</dt>
<dd>
</dd>
<dt>
tags
</dt>
<dd>
</dd>
<dt id="nb-jupyter-label">
jupyter
</dt>
<dd id="nb-jupyter-desc">
Official Jupyter Metadata for Raw Cells
</dd>
<dd>
<dl>
<dt>
source hidden
</dt>
<dd>
Whether the source is hidden.
</dd>
<dt>
outputs hidden
</dt>
<dd>
Whether the outputs are hidden.
</dd>
<dt id="nb-execution-label">
execution
</dt>
<dd id="nb-execution-desc">
Execution time for the code in the cell. This tracks time at which messages are received from iopub or shell channels
</dd>
<dd>
<dl aria-labelledby="nb-execution-label">
<dt>
execute input
</dt>
<dd>
{'description': "header.date (in ISO 8601 format) of iopub channel's execute_input message. It indicates the time at which the kernel broadcasts an execute_input message to connected frontends", 'type': 'string'}
</dd>
<dt>
execute reply
</dt>
<dd>
</dd>
</dl>
</dd>
<dt>
collapsed
</dt>
<dd>
Whether the cell's output is collapsed/expanded.
</dd>
<dt>
scrolled
</dt>
<dd>
Whether the cell's output is scrolled, unscrolled, or autoscrolled.
</dd>
</dl>
</dd>
</dl>
</dd>
<dt id="nb-cell_type-label">
cell type
</dt>
<dd>
the are three kinds of cells
<dl aria-labelledby="nb-cell_type-label">
<dt id="nb-code-label">
code
</dt>
<dd id="nb-code-desc">
Notebook code cell.
</dd>
<dt id="nb-markdown-label">
markdown
</dt>
<dd id="nb-markdown-desc">
Notebook markdown cell.
</dd>
<dt id="nb-raw-label">
raw
</dt>
<dd id="nb-raw-desc">
Notebook raw nbconvert cell.
</dd>
</dl>
</dd>
<dd id="nb-cell_type-desc">
String identifying the type of cell.
</dd>
<dt id="nb-execution_count-label">
execution count
</dt>
<dd id="nb-execution_count-desc">
The code cell's prompt number. Will be null if the cell has not been run.
</dd>
<dt id="nb-outputs-label">
outputs
</dt>
<dd id="nb-outputs-desc">
Execution, display, or stream outputs.
</dd>
<dt id="nb-attachments-label">
attachments
</dt>
<dd id="nb-attachments-desc">
Media attachments (e.g. inline images), stored as mimebundle keyed by filename.
</dd>
<dt id="nb-loc-label">
lines of code
</dt>
<dd>
lines of code in the cell, including whitespace.
</dd>
</dl>
</dd>
<dt id="nb-toolbar-label">
toolbar
</dt>
<dd>
composite widgets that allow for arrow key navigation
</dd>
</dl>
</form>
</dialog>
<button accesskey="?" aria-controls="nb-help" onclick="openDialog()">
help
</button>
</section>
<footer>
<p>
By STScI
</p>
<p>
&copy; Copyright 2022-2024
</p>
<details class="log">
<summary>
<span>
activity log
</span>
</summary>
</details>
<table aria-live="polite">
<tbody>
<tr>
<th hidden>
time
</th>
<td>
message
</td>
</tr>
</tbody>
</table>
<a accesskey="0" href="#TOP">
skip to top
</a>
</footer>
</body>
</html>
" width="100%">
</iframe>
<iframe height="600" srcdoc="<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta content="width=device-width, initial-scale=1.0" name="viewport">
<title>
Notebook
</title>
<meta content="light" name="color-scheme">
<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js">
</script>
<style type="text/css">
/* # accessible css configurations
we define these high level variables to allow for configuration during use and testing.
we are not sure what the preferred default values are for these any accessible notebooks features.
the variables specify critical degrees of freedom in the notebook style.
## notebook interface specific css variables
*/
:root {
--nb-focus-width: 3px;
--nb-accent-color: auto;
--nb-background-color-dark: #2b2a33;
--nb-background-color-light: #FFFFFF;
--nb-margin: 5%;
--nb-font-size: 16px;
--nb-font-family: serif;
--nb-line-height: 1.5;
}
/* map css variables to css properties on the `body` elements.
this mapping turns out css variables into user interfaces. */
body {
font-size: var(--nb-font-size);
font-family: var(--nb-font-family);
accent-color: var(--nb-accent-color);
margin-left: var(--nb-margin);
margin-right: var(--nb-margin);
line-height: var(--nb-line-height);
width: calc(100% - 2*var(--nb-margin));
}
/* ## notebook settings
the notebook settings interface connects configurable css features to the user.
we use dialog elements to represents an element that should be hidden by default.
it turns out there are two ways to layout `dialog` elements.
they can be used as modals or block elements.
we extract this dual purpose to provide both configurations to the user.
*/
dialog form>* {
display: block;
}
#nb-dialogs details[open]~dialog {
position: relative;
}
/* ## wcag accessibility guidelines as a feature
flexibility is the future of assistive interfaces.
it is not in the purvey of privileged developers and inaccessible systems to decide what priority accessibility is.
it is the fucking priority.
in this implementation, we want to satisfy broad user needs by making a lot of features configurable.
one configurable feature is the accessibility priority.
experienced developers may need less assistive features as they spend more time with an interface.
### buttons sizes
WCAG 2.1 defines a huge stinking AAA hit size for buttons. its beautiful.
/* satisfy AAA 2.5.5 */
.wcag-aaa button,
.wcag-aaa input[type=checkbox] {
min-height: 44px;
min-width: 44px;
}
.wcag-aaa a {
font-size: max(44px, var(--nb-font-size));
}
/* WCAG 2.2 clarified a AA button size that is used to style checkboxes and buttons. */
/* satisfy AA 2.5.8 minimum target requirement */
.wcag-aa button,
.wcag-aa input[type=checkbox] {
min-height: 24px;
min-width: 24px;
}
.wcag-aa a {
font-size: max(24px, var(--nb-font-size));
}
/* ### native textareas for AAA priority.
our AAA priority choices often ensure that third party libraries can not compromise the accessibility of notebook.
the design decisions are focused native elements so that the assistive technology users have consistent expectations and outcomes.
*/
.markdown textarea[name=source]+[role=group],
.wcag-a textarea[name=source],
.wcag-aa textarea[name=source],
.wcag-aaa .markdown textarea[name=source],
/* this group selector is very ambiguous */
.wcag-aaa textarea[name=source]+[role=group] {
display: none;
}
/* this is only needed for the lsit template variant */
ol#cells {
margin: unset;
padding: unset;
}
li.cell {
list-style-type: none;
}
/* ## notebook components layout */
td[role="none"]:not([hidden]) {
display: unset;
}
table {
border-spacing: unset;
}
main>.cell,
#cells .cell,
#cells > tbody {
display: flex;
flex-direction: column;
}
ol[reversed]#cells,
[reversed]#cells tbody {
flex-direction: column-reverse;
}
/* hide the visual output area when there are no outputs.
assistive technologies will recieve an audible notification that there are not outputs. */
fieldset[data-outputs="0"] {
display: none;
}
/* ## modifications to html defaults
### focus
there nothing to be said about this topic that [sara soueidan](https://www.sarasoueidan.com/blog/focus-indicators/) hasn't said.
we start with her [universal focus recommendation](https://www.sarasoueidan.com/blog/focus-indicators/#a-%E2%80%98universal%E2%80%99-focus-indicator).
*/
.cell:focus-within,
:focus-visible {
outline: max(var(--nb-focus-width), 1px) solid;
box-shadow: 0 0 0 calc(2 * max(var(--nb-focus-width), 1px));
}
/* on firefox, the input and output become interactive when there is overflow. chrome fixed this recently find reference.*/
textarea[name=source],
.cell>td>details {
overflow: auto;
min-width: 0;
}
#nb-settings li::marker,
summary[inert]::marker {
content: "";
}
/* ### inheriting styles */
input,
select,
button {
font-family: inherit;
font-size: inherit;
}
textarea {
font-family: monospace;
font-size: inherit;
line-height: inherit;
overflow: auto;
color: unset;
}
textarea[name=source] {
box-sizing: border-box;
width: 100%;
resize: vertical;
}
/* align checkboxes with buttons */
input[type="checkbox"] {
vertical-align: middle;
}
/* ## custom class selectors */
#nb-dialogs details:not([open])~dialog:not([open]):not(:focus-within):not(:active),
/* legend:not(:focus-within):not(:active), */
details.log:not([open])+table,
.visually-hidden:not(:focus-within):not(:active) {
clip: rect(0 0 0 0);
clip-path: inset(50%);
height: 1px;
overflow: hidden;
position: absolute;
white-space: nowrap;
width: 1px;
}
</style>
<style id="nb-light-theme" media="screen" type="text/css">
/** accessible pygments github-light-high-contrast generated by __main__**/
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
.hll { background-color: #0969da4a }
.c { color: #66707b } /* Comment */
.err { color: #a0111f } /* Error */
.k { color: #a0111f } /* Keyword */
.l { color: #702c00 } /* Literal */
.n { color: #622cbc } /* Name */
.o { color: #024c1a } /* Operator */
.p { color: #24292f } /* Punctuation */
.ch { color: #66707b } /* Comment.Hashbang */
.cm { color: #66707b } /* Comment.Multiline */
.cp { color: #66707b } /* Comment.Preproc */
.cpf { color: #66707b } /* Comment.PreprocFile */
.c1 { color: #66707b } /* Comment.Single */
.cs { color: #66707b } /* Comment.Special */
.gd { color: #023b95 } /* Generic.Deleted */
.ge { font-style: italic } /* Generic.Emph */
.gr { color: #a0111f } /* Generic.Error */
.gh { color: #023b95 } /* Generic.Heading */
.gs { font-weight: bold } /* Generic.Strong */
.gu { color: #023b95 } /* Generic.Subheading */
.kc { color: #023b95 } /* Keyword.Constant */
.kd { color: #a0111f } /* Keyword.Declaration */
.kn { color: #a0111f } /* Keyword.Namespace */
.kp { color: #a0111f } /* Keyword.Pseudo */
.kr { color: #a0111f } /* Keyword.Reserved */
.kt { color: #a0111f } /* Keyword.Type */
.ld { color: #702c00 } /* Literal.Date */
.m { color: #702c00 } /* Literal.Number */
.s { color: #023b95 } /* Literal.String */
.na { color: #702c00 } /* Name.Attribute */
.nb { color: #702c00 } /* Name.Builtin */
.nc { color: #023b95 } /* Name.Class */
.no { color: #023b95 } /* Name.Constant */
.nd { color: #702c00 } /* Name.Decorator */
.ni { color: #024c1a } /* Name.Entity */
.ne { color: #622cbc } /* Name.Exception */
.nf { color: #023b95 } /* Name.Function */
.nl { color: #702c00 } /* Name.Label */
.nn { color: #24292f } /* Name.Namespace */
.nx { color: #622cbc } /* Name.Other */
.py { color: #023b95 } /* Name.Property */
.nt { color: #024c1a } /* Name.Tag */
.nv { color: #702c00 } /* Name.Variable */
.ow { color: #622cbc } /* Operator.Word */
.pm { color: #24292f } /* Punctuation.Marker */
.w { color: #24292f } /* Text.Whitespace */
.mb { color: #702c00 } /* Literal.Number.Bin */
.mf { color: #702c00 } /* Literal.Number.Float */
.mh { color: #702c00 } /* Literal.Number.Hex */
.mi { color: #702c00 } /* Literal.Number.Integer */
.mo { color: #702c00 } /* Literal.Number.Oct */
.sa { color: #023b95 } /* Literal.String.Affix */
.sb { color: #023b95 } /* Literal.String.Backtick */
.sc { color: #023b95 } /* Literal.String.Char */
.dl { color: #023b95 } /* Literal.String.Delimiter */
.sd { color: #023b95 } /* Literal.String.Doc */
.s2 { color: #023b95 } /* Literal.String.Double */
.se { color: #023b95 } /* Literal.String.Escape */
.sh { color: #023b95 } /* Literal.String.Heredoc */
.si { color: #023b95 } /* Literal.String.Interpol */
.sx { color: #023b95 } /* Literal.String.Other */
.sr { color: #023b95 } /* Literal.String.Regex */
.s1 { color: #023b95 } /* Literal.String.Single */
.ss { color: #023b95 } /* Literal.String.Symbol */
.bp { color: #702c00 } /* Name.Builtin.Pseudo */
.fm { color: #023b95 } /* Name.Function.Magic */
.vc { color: #702c00 } /* Name.Variable.Class */
.vg { color: #702c00 } /* Name.Variable.Global */
.vi { color: #702c00 } /* Name.Variable.Instance */
.vm { color: #702c00 } /* Name.Variable.Magic */
.il { color: #702c00 } /* Literal.Number.Integer.Long */
</style>
<style id="nb-dark-theme" media="not screen" type="text/css">
/** accessible pygments github-dark-high-contrast generated by __main__**/
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
.hll { background-color: #58a6ff70 }
.c { color: #d9dee3 } /* Comment */
.err { color: #ff9492 } /* Error */
.k { color: #ff9492 } /* Keyword */
.l { color: #ffb757 } /* Literal */
.n { color: #dbb7ff } /* Name */
.o { color: #72f088 } /* Operator */
.p { color: #C9D1D9 } /* Punctuation */
.ch { color: #d9dee3 } /* Comment.Hashbang */
.cm { color: #d9dee3 } /* Comment.Multiline */
.cp { color: #d9dee3 } /* Comment.Preproc */
.cpf { color: #d9dee3 } /* Comment.PreprocFile */
.c1 { color: #d9dee3 } /* Comment.Single */
.cs { color: #d9dee3 } /* Comment.Special */
.gd { color: #91cbff } /* Generic.Deleted */
.ge { font-style: italic } /* Generic.Emph */
.gr { color: #ff9492 } /* Generic.Error */
.gh { color: #91cbff } /* Generic.Heading */
.gs { font-weight: bold } /* Generic.Strong */
.gu { color: #91cbff } /* Generic.Subheading */
.kc { color: #91cbff } /* Keyword.Constant */
.kd { color: #ff9492 } /* Keyword.Declaration */
.kn { color: #ff9492 } /* Keyword.Namespace */
.kp { color: #ff9492 } /* Keyword.Pseudo */
.kr { color: #ff9492 } /* Keyword.Reserved */
.kt { color: #ff9492 } /* Keyword.Type */
.ld { color: #ffb757 } /* Literal.Date */
.m { color: #ffb757 } /* Literal.Number */
.s { color: #91cbff } /* Literal.String */
.na { color: #ffb757 } /* Name.Attribute */
.nb { color: #ffb757 } /* Name.Builtin */
.nc { color: #91cbff } /* Name.Class */
.no { color: #91cbff } /* Name.Constant */
.nd { color: #ffb757 } /* Name.Decorator */
.ni { color: #72f088 } /* Name.Entity */
.ne { color: #dbb7ff } /* Name.Exception */
.nf { color: #91cbff } /* Name.Function */
.nl { color: #ffb757 } /* Name.Label */
.nn { color: #C9D1D9 } /* Name.Namespace */
.nx { color: #dbb7ff } /* Name.Other */
.py { color: #91cbff } /* Name.Property */
.nt { color: #72f088 } /* Name.Tag */
.nv { color: #ffb757 } /* Name.Variable */
.ow { color: #dbb7ff } /* Operator.Word */
.pm { color: #C9D1D9 } /* Punctuation.Marker */
.w { color: #C9D1D9 } /* Text.Whitespace */
.mb { color: #ffb757 } /* Literal.Number.Bin */
.mf { color: #ffb757 } /* Literal.Number.Float */
.mh { color: #ffb757 } /* Literal.Number.Hex */
.mi { color: #ffb757 } /* Literal.Number.Integer */
.mo { color: #ffb757 } /* Literal.Number.Oct */
.sa { color: #91cbff } /* Literal.String.Affix */
.sb { color: #91cbff } /* Literal.String.Backtick */
.sc { color: #91cbff } /* Literal.String.Char */
.dl { color: #91cbff } /* Literal.String.Delimiter */
.sd { color: #91cbff } /* Literal.String.Doc */
.s2 { color: #91cbff } /* Literal.String.Double */
.se { color: #91cbff } /* Literal.String.Escape */
.sh { color: #91cbff } /* Literal.String.Heredoc */
.si { color: #91cbff } /* Literal.String.Interpol */
.sx { color: #91cbff } /* Literal.String.Other */
.sr { color: #91cbff } /* Literal.String.Regex */
.s1 { color: #91cbff } /* Literal.String.Single */
.ss { color: #91cbff } /* Literal.String.Symbol */
.bp { color: #ffb757 } /* Name.Builtin.Pseudo */
.fm { color: #91cbff } /* Name.Function.Magic */
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Search the MAST Archive
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Retreive Associated Products
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<textarea aria-labelledby="cell-1-source-label nb-source-label" class="nb-source" form="cell-1" id="cell-1-source" name="source" readonly rows="20"># Large Downloads in `astroquery.mast`
***
For some programs stored in the MAST Archive, you may encounter issues when downloading data via the MAST Portal due to a large number of files. This applies particularly to JWST programs using Wide-Field Slitless Spectroscopy. It is preferable &mdash; and often, necessary &mdash; to use an API to get this data instead. In this tutorial, we'll use seemingly innocuous observations that expand into a considerable number of related files.
To that end, this notebook will demonstrate:
* Searching the MAST Portal for observations using the [astroquery.mast](https://astroquery.readthedocs.io/en/latest/mast/mast.html) API
* Retreiving associated data products, without causing a timeout error
* Downloading the desired subset of data products
## Table of Contents
* [Imports](#Imports)
* [Search the MAST Archive](#Search-the-MAST-Archive)
* [Retrieve Associated Products](#Retreive-Associated-Products)
* [Filter and Download Products](#Filter-and-Download-Products)
* [Further Reading](#Further-Reading)
## Imports
In order to run this notebook, we need:
* `astroquery.mast` to access the MAST Archive
* `astropy.table` to hold the results of our queries, combine them, and then filter them for unique products</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gh" title="Generic.Heading"># Large Downloads in `astroquery.mast`</span>
***
For some programs stored in the MAST Archive, you may encounter issues when downloading data via the MAST Portal due to a large number of files. This applies particularly to JWST programs using Wide-Field Slitless Spectroscopy. It is preferable &mdash; and often, necessary &mdash; to use an API to get this data instead. In this tutorial, we'll use seemingly innocuous observations that expand into a considerable number of related files.
To that end, this notebook will demonstrate:
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>Searching the MAST Portal for observations using the [<span class="nt" title="Name.Tag">astroquery.mast</span>](<span class="na" title="Name.Attribute">https://astroquery.readthedocs.io/en/latest/mast/mast.html</span>) API
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>Retreiving associated data products, without causing a timeout error
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>Downloading the desired subset of data products
<span class="gu" title="Generic.Subheading">## Table of Contents</span>
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Imports</span>](<span class="na" title="Name.Attribute">#Imports</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Search the MAST Archive</span>](<span class="na" title="Name.Attribute">#Search-the-MAST-Archive</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Retrieve Associated Products</span>](<span class="na" title="Name.Attribute">#Retreive-Associated-Products</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Filter and Download Products</span>](<span class="na" title="Name.Attribute">#Filter-and-Download-Products</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Further Reading</span>](<span class="na" title="Name.Attribute">#Further-Reading</span>)
<span class="gu" title="Generic.Subheading">## Imports</span>
In order to run this notebook, we need:
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`astroquery.mast`</span> to access the MAST Archive
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`astropy.table`</span> to hold the results of our queries, combine them, and then filter them for unique products
</code></pre>
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{}
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20
</td>
<td class="nb-outputs" role="none">
<h1>
Large Downloads in
<code>
astroquery.mast
</code>
</h1>
<hr>
<p>
For some programs stored in the MAST Archive, you may encounter issues when downloading data via the MAST Portal due to a large number of files. This applies particularly to JWST programs using Wide-Field Slitless Spectroscopy. It is preferable &mdash; and often, necessary &mdash; to use an API to get this data instead. In this tutorial, we'll use seemingly innocuous observations that expand into a considerable number of related files.
</p>
<p>
To that end, this notebook will demonstrate:
</p>
<ul>
<li>
Searching the MAST Portal for observations using the
<a href="https://astroquery.readthedocs.io/en/latest/mast/mast.html">
astroquery.mast
</a>
API
</li>
<li>
Retreiving associated data products, without causing a timeout error
</li>
<li>
Downloading the desired subset of data products
</li>
</ul>
<h2>
<a href="#table-of-contents">
Table of Contents
</a>
</h2>
<ul>
<li>
<a href="#Imports">
Imports
</a>
</li>
<li>
<a href="#Search-the-MAST-Archive">
Search the MAST Archive
</a>
</li>
<li>
<a href="#Retreive-Associated-Products">
Retrieve Associated Products
</a>
</li>
<li>
<a href="#Filter-and-Download-Products">
Filter and Download Products
</a>
</li>
<li>
<a href="#Further-Reading">
Further Reading
</a>
</li>
</ul>
<h2>
<a href="#imports">
Imports
</a>
</h2>
<p>
In order to run this notebook, we need:
</p>
<ul>
<li>
<code>
astroquery.mast
</code>
to access the MAST Archive
</li>
<li>
<code>
astropy.table
</code>
to hold the results of our queries, combine them, and then filter them for unique products
</li>
</ul>
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<textarea aria-labelledby="cell-2-source-label nb-source-label" class="nb-source" form="cell-2" id="cell-2-source" name="source" readonly rows="2">from astroquery.mast import Observations
from astropy.table import unique, vstack, Table</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astroquery.mast</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">Observations</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astropy.table</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">unique</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">vstack</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">Table</span>
</code></pre>
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<textarea aria-labelledby="cell-3-source-label nb-source-label" class="nb-source" form="cell-3" id="cell-3-source" name="source" readonly rows="4">## Search the MAST Archive
The first step to downloading the data is finding the observations we're interested in. This is easiest to do using `query_criteria`, which allows us to specify criteria such as RA/Dec, filters, exposure time, and any other fields listed [here](https://mast.stsci.edu/api/v0/_c_a_o_mfields.html).
In this example, we use `query_criteria` to find NIRCam observations from [JWST Program 1073](https://www.stsci.edu/cgi-bin/get-proposal-info?id=1073&amp;observatory=JWST). When querying for JWST data, using `obs_collection = 'JWST'` greatly inreases the speed of the search by decreasing the number of potential matches. This applies to all mission available in MAST, including HST.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## Search the MAST Archive</span>
The first step to downloading the data is finding the observations we're interested in. This is easiest to do using <span class="sb" title="Literal.String.Backtick">`query_criteria`</span>, which allows us to specify criteria such as RA/Dec, filters, exposure time, and any other fields listed [<span class="nt" title="Name.Tag">here</span>](<span class="na" title="Name.Attribute">https://mast.stsci.edu/api/v0/_c_a_o_mfields.html</span>).
In this example, we use <span class="sb" title="Literal.String.Backtick">`query_criteria`</span> to find NIRCam observations from [<span class="nt" title="Name.Tag">JWST Program 1073</span>](<span class="na" title="Name.Attribute">https://www.stsci.edu/cgi-bin/get-proposal-info?id=1073&amp;observatory=JWST</span>). When querying for JWST data, using <span class="sb" title="Literal.String.Backtick">`obs_collection = 'JWST'`</span> greatly inreases the speed of the search by decreasing the number of potential matches. This applies to all mission available in MAST, including HST.
</code></pre>
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<h2>
<a href="#search-the-mast-archive">
Search the MAST Archive
</a>
</h2>
<p>
The first step to downloading the data is finding the observations we're interested in. This is easiest to do using
<code>
query_criteria
</code>
, which allows us to specify criteria such as RA/Dec, filters, exposure time, and any other fields listed
<a href="https://mast.stsci.edu/api/v0/_c_a_o_mfields.html">
here
</a>
.
</p>
<p>
In this example, we use
<code>
query_criteria
</code>
to find NIRCam observations from
<a href="https://www.stsci.edu/cgi-bin/get-proposal-info?id=1073&amp;observatory=JWST">
JWST Program 1073
</a>
. When querying for JWST data, using
<code>
obs_collection = 'JWST'
</code>
greatly inreases the speed of the search by decreasing the number of potential matches. This applies to all mission available in MAST, including HST.
</p>
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<textarea aria-labelledby="cell-4-source-label nb-source-label" class="nb-source" form="cell-4" id="cell-4-source" name="source" readonly rows="5">matched_obs = Observations.query_criteria(
obs_collection = 'JWST'
, proposal_id = '1073'
, instrument_name = 'NIRCAM/IMAGE' # Be sure to specify the full "instrument/mode" configuration!
)</textarea>
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<pre><span></span><code><span class="n" title="Name">matched_obs</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">query_criteria</span><span class="p" title="Punctuation">(</span>
<span class="n" title="Name">obs_collection</span> <span class="o" title="Operator">=</span> <span class="s1" title="Literal.String.Single">'JWST'</span>
<span class="p" title="Punctuation">,</span> <span class="n" title="Name">proposal_id</span> <span class="o" title="Operator">=</span> <span class="s1" title="Literal.String.Single">'1073'</span>
<span class="p" title="Punctuation">,</span> <span class="n" title="Name">instrument_name</span> <span class="o" title="Operator">=</span> <span class="s1" title="Literal.String.Single">'NIRCAM/IMAGE'</span> <span class="c1" title="Comment.Single"># Be sure to specify the full "instrument/mode" configuration!</span>
<span class="p" title="Punctuation">)</span>
</code></pre>
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<textarea aria-labelledby="cell-5-source-label nb-source-label" class="nb-source" form="cell-5" id="cell-5-source" name="source" readonly rows="3"># This displays selected columns from the observation table, as a sanity check
columns = ['dataproduct_type', 'filters', 'calib_level', 't_exptime', 'proposal_pi', 'intentType', 'obsid','instrument_name']
matched_obs[columns].show_in_notebook(display_length=5)</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># This displays selected columns from the observation table, as a sanity check</span>
<span class="n" title="Name">columns</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'dataproduct_type'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'filters'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'calib_level'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'t_exptime'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'proposal_pi'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'intentType'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'obsid'</span><span class="p" title="Punctuation">,</span><span class="s1" title="Literal.String.Single">'instrument_name'</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">matched_obs</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">columns</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">show_in_notebook</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">display_length</span><span class="o" title="Operator">=</span><span class="mi" title="Literal.Number.Integer">5</span><span class="p" title="Punctuation">)</span>
</code></pre>
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<textarea aria-labelledby="cell-6-source-label nb-source-label" class="nb-source" form="cell-6" id="cell-6-source" name="source" readonly rows="6">The above search results in 15 observations. Keep this in the number in mind as we search for associated products.
## Retreive Associated Products
Each observation has associated data products. Which products are of interest to you depends on how you intend to use the data; more on this in the section below. For now, let's retreive all the products by requesting them in small "chunks".
**Note: It is wise to avoid requesting all of the products simultaneously.** This is extremely likely to take an enormous amount of time, fail, or worse, do both, ultimately giving you a headache. MAST offers no medical advice, but we are decidedly anti-headache. Requesting products in groups of five offers the best balance between speed and reliability.</textarea>
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<pre><span></span><code>The above search results in 15 observations. Keep this in the number in mind as we search for associated products.
<span class="gu" title="Generic.Subheading">## Retreive Associated Products</span>
Each observation has associated data products. Which products are of interest to you depends on how you intend to use the data; more on this in the section below. For now, let's retreive all the products by requesting them in small "chunks".
<span class="gs" title="Generic.Strong">**Note: It is wise to avoid requesting all of the products simultaneously.**</span> This is extremely likely to take an enormous amount of time, fail, or worse, do both, ultimately giving you a headache. MAST offers no medical advice, but we are decidedly anti-headache. Requesting products in groups of five offers the best balance between speed and reliability.
</code></pre>
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<p>
The above search results in 15 observations. Keep this in the number in mind as we search for associated products.
</p>
<h2>
<a href="#retreive-associated-products">
Retreive Associated Products
</a>
</h2>
<p>
Each observation has associated data products. Which products are of interest to you depends on how you intend to use the data; more on this in the section below. For now, let's retreive all the products by requesting them in small "chunks".
</p>
<p>
<strong>
Note: It is wise to avoid requesting all of the products simultaneously.
</strong>
This is extremely likely to take an enormous amount of time, fail, or worse, do both, ultimately giving you a headache. MAST offers no medical advice, but we are decidedly anti-headache. Requesting products in groups of five offers the best balance between speed and reliability.
</p>
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<textarea aria-labelledby="cell-7-source-label nb-source-label" class="nb-source" form="cell-7" id="cell-7-source" name="source" readonly rows="12"># Split the observations into "chunks" of size five
sz_chunk = 5
chunks = [matched_obs[i:i+sz_chunk] for i in range(0,len(matched_obs), sz_chunk)]
# Get the list of products for each chunk
t = [Observations.get_product_list(chunk) for chunk in chunks]
# Keep only the unique files
files = unique(vstack(t), keys='productFilename')
# How many files are there? How large are they?
print(f"There are {len(files)} unique files, which are {sum(files['size'])/10**9:.1f} GB in size.")</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Split the observations into "chunks" of size five</span>
<span class="n" title="Name">sz_chunk</span> <span class="o" title="Operator">=</span> <span class="mi" title="Literal.Number.Integer">5</span>
<span class="n" title="Name">chunks</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="n" title="Name">matched_obs</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">i</span><span class="p" title="Punctuation">:</span><span class="n" title="Name">i</span><span class="o" title="Operator">+</span><span class="n" title="Name">sz_chunk</span><span class="p" title="Punctuation">]</span> <span class="k" title="Keyword">for</span> <span class="n" title="Name">i</span> <span class="ow" title="Operator.Word">in</span> <span class="nb" title="Name.Builtin">range</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">,</span><span class="nb" title="Name.Builtin">len</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">matched_obs</span><span class="p" title="Punctuation">),</span> <span class="n" title="Name">sz_chunk</span><span class="p" title="Punctuation">)]</span>
<span class="c1" title="Comment.Single"># Get the list of products for each chunk</span>
<span class="n" title="Name">t</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">get_product_list</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">chunk</span><span class="p" title="Punctuation">)</span> <span class="k" title="Keyword">for</span> <span class="n" title="Name">chunk</span> <span class="ow" title="Operator.Word">in</span> <span class="n" title="Name">chunks</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># Keep only the unique files</span>
<span class="n" title="Name">files</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">unique</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">vstack</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">t</span><span class="p" title="Punctuation">),</span> <span class="n" title="Name">keys</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'productFilename'</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># How many files are there? How large are they?</span>
<span class="nb" title="Name.Builtin">print</span><span class="p" title="Punctuation">(</span><span class="sa" title="Literal.String.Affix">f</span><span class="s2" title="Literal.String.Double">"There are </span><span class="si" title="Literal.String.Interpol">{</span><span class="nb" title="Name.Builtin">len</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">files</span><span class="p" title="Punctuation">)</span><span class="si" title="Literal.String.Interpol">}</span><span class="s2" title="Literal.String.Double"> unique files, which are </span><span class="si" title="Literal.String.Interpol">{</span><span class="nb" title="Name.Builtin">sum</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">files</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'size'</span><span class="p" title="Punctuation">])</span><span class="o" title="Operator">/</span><span class="mi" title="Literal.Number.Integer">10</span><span class="o" title="Operator">**</span><span class="mi" title="Literal.Number.Integer">9</span><span class="si" title="Literal.String.Interpol">:</span><span class="s2" title="Literal.String.Double">.1f</span><span class="si" title="Literal.String.Interpol">}</span><span class="s2" title="Literal.String.Double"> GB in size."</span><span class="p" title="Punctuation">)</span>
</code></pre>
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<textarea aria-labelledby="cell-8-source-label nb-source-label" class="nb-source" form="cell-8" id="cell-8-source" name="source" readonly rows="3">Now the issue with requesting all of the products simultaneously is clear: there are more than 6,000 unique files associated with our 15 observations.
Running this search on the MAST Portal results in over 30,000 files since the Portal does not exclude duplicate results; that is nearing the limit of the what the Portal can load. One of the advantages of using the API is avoiding this large number of duplicates.</textarea>
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<pre><span></span><code>Now the issue with requesting all of the products simultaneously is clear: there are more than 6,000 unique files associated with our 15 observations.
Running this search on the MAST Portal results in over 30,000 files since the Portal does not exclude duplicate results; that is nearing the limit of the what the Portal can load. One of the advantages of using the API is avoiding this large number of duplicates.
</code></pre>
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<p>
Now the issue with requesting all of the products simultaneously is clear: there are more than 6,000 unique files associated with our 15 observations.
</p>
<p>
Running this search on the MAST Portal results in over 30,000 files since the Portal does not exclude duplicate results; that is nearing the limit of the what the Portal can load. One of the advantages of using the API is avoiding this large number of duplicates.
</p>
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<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-9-select">
cell type
<select form="cell-9" name="cell_type">
<option id="cell-9-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
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<textarea aria-labelledby="cell-9-source-label nb-source-label" class="nb-source" form="cell-9" id="cell-9-source" name="source" readonly rows="7">## Filter and Download Products
If you are trying to download proprietary data, you will need to login. This requires a MAST token, which you can create at the [auth.mast](#https://auth.mast.stsci.edu/tokens) wesbite. If you have not set this as environment variable, you will have to enter it in the login prompt below.
In this example, we are looking to download the uncalibrated products. We will filter those out below using the `productSubGroupDescription` field. You can find the other available product filters, including product type and file size, [here](https://mast.stsci.edu/api/v0/_productsfields.html). Examples are also included, but commented out, in the cell below.
An additional option we make use of is the `curl_script` flag. Rather than downloading the data immediately, this method instead downloads a curl script. This is turned off by default, but is more robust than a direct download, and is highly recommended when downloading a large number of files. You can run this script using `bash mastDownload_dddd.sh`, changing `dddd` to reflect the actual name of your file.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## Filter and Download Products</span>
If you are trying to download proprietary data, you will need to login. This requires a MAST token, which you can create at the [<span class="nt" title="Name.Tag">auth.mast</span>](<span class="na" title="Name.Attribute">#https://auth.mast.stsci.edu/tokens</span>) wesbite. If you have not set this as environment variable, you will have to enter it in the login prompt below.
In this example, we are looking to download the uncalibrated products. We will filter those out below using the <span class="sb" title="Literal.String.Backtick">`productSubGroupDescription`</span> field. You can find the other available product filters, including product type and file size, [<span class="nt" title="Name.Tag">here</span>](<span class="na" title="Name.Attribute">https://mast.stsci.edu/api/v0/_productsfields.html</span>). Examples are also included, but commented out, in the cell below.
An additional option we make use of is the <span class="sb" title="Literal.String.Backtick">`curl_script`</span> flag. Rather than downloading the data immediately, this method instead downloads a curl script. This is turned off by default, but is more robust than a direct download, and is highly recommended when downloading a large number of files. You can run this script using <span class="sb" title="Literal.String.Backtick">`bash mastDownload_dddd.sh`</span>, changing <span class="sb" title="Literal.String.Backtick">`dddd`</span> to reflect the actual name of your file.
</code></pre>
</div>
</div>
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<button formmethod="dialog">
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<pre><code>
{}
</code></pre>
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<td class="nb-loc" hidden id="cell-9-loc" role="none">
7
</td>
<td class="nb-outputs" role="none">
<h2>
<a href="#filter-and-download-products">
Filter and Download Products
</a>
</h2>
<p>
If you are trying to download proprietary data, you will need to login. This requires a MAST token, which you can create at the
<a href="#https://auth.mast.stsci.edu/tokens">
auth.mast
</a>
wesbite. If you have not set this as environment variable, you will have to enter it in the login prompt below.
</p>
<p>
In this example, we are looking to download the uncalibrated products. We will filter those out below using the
<code>
productSubGroupDescription
</code>
field. You can find the other available product filters, including product type and file size,
<a href="https://mast.stsci.edu/api/v0/_productsfields.html">
here
</a>
. Examples are also included, but commented out, in the cell below.
</p>
<p>
An additional option we make use of is the
<code>
curl_script
</code>
flag. Rather than downloading the data immediately, this method instead downloads a curl script. This is turned off by default, but is more robust than a direct download, and is highly recommended when downloading a large number of files. You can run this script using
<code>
bash mastDownload_dddd.sh
</code>
, changing
<code>
dddd
</code>
to reflect the actual name of your file.
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 10 cell-10-cell_type" class="cell code" data-index="10" data-loc="10" role="listitem">
<td class="nb-anchor" role="none">
<a aria-describedby="nb-code-label nb-cell-label cell-10-loc nb-loc-label" aria-labelledby="nb-cell-label 10" class="nb-anchor" href="#10" id="10">
10
</a>
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<label class="nb-execution_count" id="cell-10-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
None
</span>
<span aria-hidden="true">
]
</span>
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</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-10-select">
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</option>
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<textarea aria-labelledby="cell-10-source-label nb-source-label" class="nb-source" form="cell-10" id="cell-10-source" name="source" readonly rows="10"># Un-comment below if downloading data during its exclusive access period.
#Observations.login()
manifest = Observations.download_products(
files
, productSubGroupDescription='UNCAL'
, curl_flag=True
#, dataproduct_type='IMAGE'
#, calib_level = [2]
)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="c1" title="Comment.Single"># Un-comment below if downloading data during its exclusive access period.</span>
<span class="c1" title="Comment.Single">#Observations.login()</span>
<span class="n" title="Name">manifest</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">download_products</span><span class="p" title="Punctuation">(</span>
<span class="n" title="Name">files</span>
<span class="p" title="Punctuation">,</span> <span class="n" title="Name">productSubGroupDescription</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'UNCAL'</span>
<span class="p" title="Punctuation">,</span> <span class="n" title="Name">curl_flag</span><span class="o" title="Operator">=</span><span class="kc" title="Keyword.Constant">True</span>
<span class="c1" title="Comment.Single">#, dataproduct_type='IMAGE'</span>
<span class="c1" title="Comment.Single">#, calib_level = [2]</span>
<span class="p" title="Punctuation">)</span>
</code></pre>
</div>
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Close
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<pre><code>
{}
</code></pre>
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10
</td>
<td class="nb-outputs" role="none">
<fieldset class="nb-outputs" data-outputs="0">
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<span>
Out
</span>
<span aria-hidden="true">
[
</span>
<span>
None
</span>
<span aria-hidden="true">
]
</span>
</legend>
<span class="visually-hidden" id="cell-10-outputs-len">
has 0 output
</span>
</fieldset>
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11
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<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-11-select">
cell type
<select form="cell-11" name="cell_type">
<option id="cell-11-cell_type" selected value="markdown">
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</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
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<textarea aria-labelledby="cell-11-source-label nb-source-label" class="nb-source" form="cell-11" id="cell-11-source" name="source" readonly rows="1">All of the code in this notebook is available as a 'companion script', for further convenience.</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code>All of the code in this notebook is available as a 'companion script', for further convenience.
</code></pre>
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{}
</code></pre>
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1
</td>
<td class="nb-outputs" role="none">
<p>
All of the code in this notebook is available as a 'companion script', for further convenience.
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 12 cell-12-cell_type" class="cell markdown" data-index="12" data-loc="4" role="listitem">
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<a aria-describedby="nb-markdown-label nb-cell-label cell-12-loc nb-loc-label" aria-labelledby="nb-cell-label 12" class="nb-anchor" href="#12" id="12">
12
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<label class="nb-execution_count" id="cell-12-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
</label>
</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-12-select">
cell type
<select form="cell-12" name="cell_type">
<option id="cell-12-cell_type" selected value="markdown">
markdown
</option>
<option value="code">
code
</option>
<option value="raw">
raw
</option>
</select>
</label>
</td>
<td class="nb-toolbar" hidden role="none">
<form aria-labelledby="cell-12-source-label" class="nb-toolbar" hidden id="cell-12" name="cell-12">
<fieldset>
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<td class="nb-source" hidden role="none">
<textarea aria-labelledby="cell-12-source-label nb-source-label" class="nb-source" form="cell-12" id="cell-12-source" name="source" readonly rows="4">## Futher Reading
* For a full explanation of product levels and the processing pipleline, see [Science Data Products](https://outerspace.stsci.edu/display/MASTDOCS/Science+Data+Products)
* [JWST Archive Manual](https://outerspace.stsci.edu/display/MASTDOCS/JWST+Archive+Manual)
* [Astropy](https://www.astropy.org) and the relevant [Table](https://docs.astropy.org/en/stable/api/astropy.table.Table.html?highlight=table) object</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## Futher Reading</span>
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>For a full explanation of product levels and the processing pipleline, see [<span class="nt" title="Name.Tag">Science Data Products</span>](<span class="na" title="Name.Attribute">https://outerspace.stsci.edu/display/MASTDOCS/Science+Data+Products</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">JWST Archive Manual</span>](<span class="na" title="Name.Attribute">https://outerspace.stsci.edu/display/MASTDOCS/JWST+Archive+Manual</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Astropy</span>](<span class="na" title="Name.Attribute">https://www.astropy.org</span>) and the relevant [<span class="nt" title="Name.Tag">Table</span>](<span class="na" title="Name.Attribute">https://docs.astropy.org/en/stable/api/astropy.table.Table.html?highlight=table</span>) object
</code></pre>
</div>
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<pre><code>
{}
</code></pre>
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<td class="nb-loc" hidden id="cell-12-loc" role="none">
4
</td>
<td class="nb-outputs" role="none">
<h2>
<a href="#futher-reading">
Futher Reading
</a>
</h2>
<ul>
<li>
For a full explanation of product levels and the processing pipleline, see
<a href="https://outerspace.stsci.edu/display/MASTDOCS/Science+Data+Products">
Science Data Products
</a>
</li>
<li>
<a href="https://outerspace.stsci.edu/display/MASTDOCS/JWST+Archive+Manual">
JWST Archive Manual
</a>
</li>
<li>
<a href="https://www.astropy.org">
Astropy
</a>
and the relevant
<a href="https://docs.astropy.org/en/stable/api/astropy.table.Table.html?highlight=table">
Table
</a>
object
</li>
</ul>
</td>
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13
</a>
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<label class="nb-execution_count" id="cell-13-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
</span>
<span aria-hidden="true">
]
</span>
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</td>
<td class="nb-cell_type" hidden role="none">
<label class="nb-cell_type" id="nb-cell-13-select">
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<select form="cell-13" name="cell_type">
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</option>
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<textarea aria-labelledby="cell-13-source-label nb-source-label" class="nb-source" form="cell-13" id="cell-13-source" name="source" readonly rows="10">## About this Notebook
**Authors:** Thomas Dutkiewicz, Dick Shaw &lt;br&gt;
**Keywords:** Downloads, astroquery, MAST &lt;br&gt;
**Last Updated:** Aug 2022 &lt;br&gt;
**Next Review Date:** Feb 2023
***
[Top of Page](#top)
&lt;img style="float: right;" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" alt="Space Telescope Logo" width="200px"/&gt; </textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu" title="Generic.Subheading">## About this Notebook</span>
<span class="gs" title="Generic.Strong">**Authors:**</span> Thomas Dutkiewicz, Dick Shaw &lt;br&gt;
<span class="gs" title="Generic.Strong">**Keywords:**</span> Downloads, astroquery, MAST &lt;br&gt;
<span class="gs" title="Generic.Strong">**Last Updated:**</span> Aug 2022 &lt;br&gt;
<span class="gs" title="Generic.Strong">**Next Review Date:**</span> Feb 2023
***
[<span class="nt" title="Name.Tag">Top of Page</span>](<span class="na" title="Name.Attribute">#top</span>)
&lt;img style="float: right;" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" alt="Space Telescope Logo" width="200px"/&gt;
</code></pre>
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10
</td>
<td class="nb-outputs" role="none">
<h2>
<a href="#about-this-notebook">
About this Notebook
</a>
</h2>
<p>
<strong>
Authors:
</strong>
Thomas Dutkiewicz, Dick Shaw
<br>
<strong>
Keywords:
</strong>
Downloads, astroquery, MAST
<br>
<strong>
Last Updated:
</strong>
Aug 2022
<br>
<strong>
Next Review Date:
</strong>
Feb 2023
</p>
<hr>
<p>
<a href="#top">
Top of Page
</a>
<img alt="Space Telescope Logo" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" style="float: right;" width="200px">
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<table class="nb-cells-footer" hidden>
<tbody>
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<th scope="row">
all cells
</th>
<th scope="row">
count
</th>
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</td>
<td class="nb-cell_type">
13
</td>
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13
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</td>
<td class="nb-metadata">
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<td class="nb-loc">
71
</td>
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<td class="nb-ordered">
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13
</td>
<td class="nb-source">
</td>
<td class="nb-outputs">
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{"kernelspec": {"display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.16"}}
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notebook
</dt>
<dd>
a collections of
<a href="#nb-cells-label">
cells
</a>
</dd>
<dd>
<dl aria-labelledby="nb-notebook-label nb-defs-label">
<dt id="nb-root-metadata-label">
metadata
</dt>
<dd id="nb-root-metadata-desc">
Notebook root-level metadata.
</dd>
<dd>
<dl>
<dt id="nb-kernelspec-label">
kernelspec
</dt>
<dd>
Kernel information.
</dd>
<dd>
<dl aria-labelledby="nb-kernelspec-label">
<dt>
kernel name
</dt>
<dd>
Name of the kernel specification.
</dd>
<dt>
display name
</dt>
<dd>
Name to display in UI.
</dd>
</dl>
</dd>
<dt id="nb-language_info-label">
language info
</dt>
<dd>
Kernel information.
</dd>
<dd>
<dl aria-labelledby="nb-language_info-label">
<dt>
programming language
</dt>
<dd>
The programming language which this kernel runs.
</dd>
<dt>
<a>
codemirror mode
</a>
</dt>
<dd>
The codemirror mode to use for code in this language.
</dd>
<dt>
file extension
</dt>
<dd>
The file extension for files in this language.
</dd>
<dt>
mimetype
</dt>
<dd>
The mimetype corresponding to files in this language.
</dd>
<dt>
pygments lexer
</dt>
<dd>
The pygments lexer to use for code in this language.
</dd>
</dl>
</dd>
<dt>
title
</dt>
<dd>
The title of the notebook document
</dd>
<dt>
authors
</dt>
<dd>
The author(s) of the notebook document
</dd>
</dl>
</dd>
<dt id="nb-cells-label">
cells
</dt>
<dd id="nb-cells-desc">
Array of cells of the current notebook.
</dd>
</dl>
</dd>
<dt id="nb-cell-label">
cell
</dt>
<dd>
<dl aria-labelledby="nb-cell-label nb-defs-label">
<dt id="nb-index-label">
index
</dt>
<dd id="nb-index-desc">
the ordinal location of the cell in the notebook, counting begins from 1.
</dd>
<dt id="nb-source-label">
source
</dt>
<dd id="nb-source-desc">
Contents of the cell, represented as an array of lines.
</dd>
<dt id="nb-metadata-label">
metadata
</dt>
<dd id="nb-metadata-desc">
Cell-level metadata.
</dd>
<dd>
<dl aria-labelledby="nb-metadata-label">
<dt>
name
</dt>
<dd>
</dd>
<dt>
tags
</dt>
<dd>
</dd>
<dt id="nb-jupyter-label">
jupyter
</dt>
<dd id="nb-jupyter-desc">
Official Jupyter Metadata for Raw Cells
</dd>
<dd>
<dl>
<dt>
source hidden
</dt>
<dd>
Whether the source is hidden.
</dd>
<dt>
outputs hidden
</dt>
<dd>
Whether the outputs are hidden.
</dd>
<dt id="nb-execution-label">
execution
</dt>
<dd id="nb-execution-desc">
Execution time for the code in the cell. This tracks time at which messages are received from iopub or shell channels
</dd>
<dd>
<dl aria-labelledby="nb-execution-label">
<dt>
execute input
</dt>
<dd>
{'description': "header.date (in ISO 8601 format) of iopub channel's execute_input message. It indicates the time at which the kernel broadcasts an execute_input message to connected frontends", 'type': 'string'}
</dd>
<dt>
execute reply
</dt>
<dd>
</dd>
</dl>
</dd>
<dt>
collapsed
</dt>
<dd>
Whether the cell's output is collapsed/expanded.
</dd>
<dt>
scrolled
</dt>
<dd>
Whether the cell's output is scrolled, unscrolled, or autoscrolled.
</dd>
</dl>
</dd>
</dl>
</dd>
<dt id="nb-cell_type-label">
cell type
</dt>
<dd>
the are three kinds of cells
<dl aria-labelledby="nb-cell_type-label">
<dt id="nb-code-label">
code
</dt>
<dd id="nb-code-desc">
Notebook code cell.
</dd>
<dt id="nb-markdown-label">
markdown
</dt>
<dd id="nb-markdown-desc">
Notebook markdown cell.
</dd>
<dt id="nb-raw-label">
raw
</dt>
<dd id="nb-raw-desc">
Notebook raw nbconvert cell.
</dd>
</dl>
</dd>
<dd id="nb-cell_type-desc">
String identifying the type of cell.
</dd>
<dt id="nb-execution_count-label">
execution count
</dt>
<dd id="nb-execution_count-desc">
The code cell's prompt number. Will be null if the cell has not been run.
</dd>
<dt id="nb-outputs-label">
outputs
</dt>
<dd id="nb-outputs-desc">
Execution, display, or stream outputs.
</dd>
<dt id="nb-attachments-label">
attachments
</dt>
<dd id="nb-attachments-desc">
Media attachments (e.g. inline images), stored as mimebundle keyed by filename.
</dd>
<dt id="nb-loc-label">
lines of code
</dt>
<dd>
lines of code in the cell, including whitespace.
</dd>
</dl>
</dd>
<dt id="nb-toolbar-label">
toolbar
</dt>
<dd>
composite widgets that allow for arrow key navigation
</dd>
</dl>
</form>
</dialog>
<button accesskey="?" aria-controls="nb-help" onclick="openDialog()">
help
</button>
</section>
<footer>
<p>
By STScI
</p>
<p>
&copy; Copyright 2022-2024
</p>
<details class="log">
<summary>
<span>
activity log
</span>
</summary>
</details>
<table aria-live="polite">
<tbody>
<tr>
<th hidden>
time
</th>
<td>
message
</td>
</tr>
</tbody>
</table>
<a accesskey="0" href="#TOP">
skip to top
</a>
</footer>
</body>
</html>
" width="100%">
</iframe>
<iframe height="600" srcdoc="<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta content="width=device-width, initial-scale=1.0" name="viewport">
<title>
Notebook
</title>
<meta content="light" name="color-scheme">
<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js">
</script>
<style type="text/css">
/* # accessible css configurations
we define these high level variables to allow for configuration during use and testing.
we are not sure what the preferred default values are for these any accessible notebooks features.
the variables specify critical degrees of freedom in the notebook style.
## notebook interface specific css variables
*/
:root {
--nb-focus-width: 3px;
--nb-accent-color: auto;
--nb-background-color-dark: #2b2a33;
--nb-background-color-light: #FFFFFF;
--nb-margin: 5%;
--nb-font-size: 16px;
--nb-font-family: serif;
--nb-line-height: 1.5;
}
/* map css variables to css properties on the `body` elements.
this mapping turns out css variables into user interfaces. */
body {
font-size: var(--nb-font-size);
font-family: var(--nb-font-family);
accent-color: var(--nb-accent-color);
margin-left: var(--nb-margin);
margin-right: var(--nb-margin);
line-height: var(--nb-line-height);
width: calc(100% - 2*var(--nb-margin));
}
/* ## notebook settings
the notebook settings interface connects configurable css features to the user.
we use dialog elements to represents an element that should be hidden by default.
it turns out there are two ways to layout `dialog` elements.
they can be used as modals or block elements.
we extract this dual purpose to provide both configurations to the user.
*/
dialog form>* {
display: block;
}
#nb-dialogs details[open]~dialog {
position: relative;
}
/* ## wcag accessibility guidelines as a feature
flexibility is the future of assistive interfaces.
it is not in the purvey of privileged developers and inaccessible systems to decide what priority accessibility is.
it is the fucking priority.
in this implementation, we want to satisfy broad user needs by making a lot of features configurable.
one configurable feature is the accessibility priority.
experienced developers may need less assistive features as they spend more time with an interface.
### buttons sizes
WCAG 2.1 defines a huge stinking AAA hit size for buttons. its beautiful.
/* satisfy AAA 2.5.5 */
.wcag-aaa button,
.wcag-aaa input[type=checkbox] {
min-height: 44px;
min-width: 44px;
}
.wcag-aaa a {
font-size: max(44px, var(--nb-font-size));
}
/* WCAG 2.2 clarified a AA button size that is used to style checkboxes and buttons. */
/* satisfy AA 2.5.8 minimum target requirement */
.wcag-aa button,
.wcag-aa input[type=checkbox] {
min-height: 24px;
min-width: 24px;
}
.wcag-aa a {
font-size: max(24px, var(--nb-font-size));
}
/* ### native textareas for AAA priority.
our AAA priority choices often ensure that third party libraries can not compromise the accessibility of notebook.
the design decisions are focused native elements so that the assistive technology users have consistent expectations and outcomes.
*/
.markdown textarea[name=source]+[role=group],
.wcag-a textarea[name=source],
.wcag-aa textarea[name=source],
.wcag-aaa .markdown textarea[name=source],
/* this group selector is very ambiguous */
.wcag-aaa textarea[name=source]+[role=group] {
display: none;
}
/* this is only needed for the lsit template variant */
ol#cells {
margin: unset;
padding: unset;
}
li.cell {
list-style-type: none;
}
/* ## notebook components layout */
td[role="none"]:not([hidden]) {
display: unset;
}
table {
border-spacing: unset;
}
main>.cell,
#cells .cell,
#cells > tbody {
display: flex;
flex-direction: column;
}
ol[reversed]#cells,
[reversed]#cells tbody {
flex-direction: column-reverse;
}
/* hide the visual output area when there are no outputs.
assistive technologies will recieve an audible notification that there are not outputs. */
fieldset[data-outputs="0"] {
display: none;
}
/* ## modifications to html defaults
### focus
there nothing to be said about this topic that [sara soueidan](https://www.sarasoueidan.com/blog/focus-indicators/) hasn't said.
we start with her [universal focus recommendation](https://www.sarasoueidan.com/blog/focus-indicators/#a-%E2%80%98universal%E2%80%99-focus-indicator).
*/
.cell:focus-within,
:focus-visible {
outline: max(var(--nb-focus-width), 1px) solid;
box-shadow: 0 0 0 calc(2 * max(var(--nb-focus-width), 1px));
}
/* on firefox, the input and output become interactive when there is overflow. chrome fixed this recently find reference.*/
textarea[name=source],
.cell>td>details {
overflow: auto;
min-width: 0;
}
#nb-settings li::marker,
summary[inert]::marker {
content: "";
}
/* ### inheriting styles */
input,
select,
button {
font-family: inherit;
font-size: inherit;
}
textarea {
font-family: monospace;
font-size: inherit;
line-height: inherit;
overflow: auto;
color: unset;
}
textarea[name=source] {
box-sizing: border-box;
width: 100%;
resize: vertical;
}
/* align checkboxes with buttons */
input[type="checkbox"] {
vertical-align: middle;
}
/* ## custom class selectors */
#nb-dialogs details:not([open])~dialog:not([open]):not(:focus-within):not(:active),
/* legend:not(:focus-within):not(:active), */
details.log:not([open])+table,
.visually-hidden:not(:focus-within):not(:active) {
clip: rect(0 0 0 0);
clip-path: inset(50%);
height: 1px;
overflow: hidden;
position: absolute;
white-space: nowrap;
width: 1px;
}
</style>
<style id="nb-light-theme" media="screen" type="text/css">
/** accessible pygments github-light-high-contrast generated by __main__**/
pre { line-height: 125%; }
td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
.hll { background-color: #0969da4a }
.c { color: #66707b } /* Comment */
.err { color: #a0111f } /* Error */
.k { color: #a0111f } /* Keyword */
.l { color: #702c00 } /* Literal */
.n { color: #622cbc } /* Name */
.o { color: #024c1a } /* Operator */
.p { color: #24292f } /* Punctuation */
.ch { color: #66707b } /* Comment.Hashbang */
.cm { color: #66707b } /* Comment.Multiline */
.cp { color: #66707b } /* Comment.Preproc */
.cpf { color: #66707b } /* Comment.PreprocFile */
.c1 { color: #66707b } /* Comment.Single */
.cs { color: #66707b } /* Comment.Special */
.gd { color: #023b95 } /* Generic.Deleted */
.ge { font-style: italic } /* Generic.Emph */
.gr { color: #a0111f } /* Generic.Error */
.gh { color: #023b95 } /* Generic.Heading */
.gs { font-weight: bold } /* Generic.Strong */
.gu { color: #023b95 } /* Generic.Subheading */
.kc { color: #023b95 } /* Keyword.Constant */
.kd { color: #a0111f } /* Keyword.Declaration */
.kn { color: #a0111f } /* Keyword.Namespace */
.kp { color: #a0111f } /* Keyword.Pseudo */
.kr { color: #a0111f } /* Keyword.Reserved */
.kt { color: #a0111f } /* Keyword.Type */
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.m { color: #702c00 } /* Literal.Number */
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.nc { color: #023b95 } /* Name.Class */
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.nf { color: #023b95 } /* Name.Function */
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.nx { color: #622cbc } /* Name.Other */
.py { color: #023b95 } /* Name.Property */
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.ow { color: #622cbc } /* Operator.Word */
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.w { color: #24292f } /* Text.Whitespace */
.mb { color: #702c00 } /* Literal.Number.Bin */
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.dl { color: #023b95 } /* Literal.String.Delimiter */
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.il { color: #702c00 } /* Literal.Number.Integer.Long */
</style>
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/** accessible pygments github-dark-high-contrast generated by __main__**/
pre { line-height: 125%; }
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span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
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span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
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.l { color: #ffb757 } /* Literal */
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.sr { color: #91cbff } /* Literal.String.Regex */
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.vg { color: #ffb757 } /* Name.Variable.Global */
.vi { color: #ffb757 } /* Name.Variable.Instance */
.vm { color: #ffb757 } /* Name.Variable.Magic */
.il { color: #ffb757 } /* Literal.Number.Integer.Long */
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<li>
<a href="#historical-quasar-observations">
Historical Quasar Observations
</a>
<ol>
<li>
<a href="#learning-goals">
Learning Goals
</a>
</li>
<li>
<a href="#introduction">
Introduction
</a>
<ol>
<li>
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Workflow
</a>
</li>
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<li>
<a href="#imports">
Imports
</a>
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</li>
<li>
<a href="#historical-observation-coverage">
Historical Observation Coverage
</a>
<ol>
<li>
<a href="#query-the-mast-archive-by-object-name">
Query the MAST Archive by Object Name
</a>
</li>
<li>
<a href="#create-plotting-variables">
Create Plotting Variables
</a>
<ol>
<li>
<a href="#time-data">
Time Data
</a>
</li>
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<a href="#wavelength-data">
Wavelength Data
</a>
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<li>
<a href="#mission-names">
Mission Names
</a>
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<a href="#plotting-historical-observation-coverage">
Plotting Historical Observation Coverage
</a>
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Plot a Spectrum
</a>
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Query HST for Spectra
</a>
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List Available Instrument and Filter Combinations
</a>
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Select Observations with a Specific Instrument and Filter Combination
</a>
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Filtering for Relevant Products
</a>
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<li>
<a href="#download-the-data-to-plot">
Download the Data to Plot
</a>
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Plot the Spectrum
</a>
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Exercises
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Recognizing Familiar Emission Lines
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<textarea aria-labelledby="cell-1-source-label nb-source-label" class="nb-source" form="cell-1" id="cell-1-source" name="source" readonly rows="31"># Historical Quasar Observations
***
## Learning Goals
By the end of this tutorial, you will:
* Understand how to query data on a target from the MAST archive.
* Be able to plot a historic observation coverage plot of a target.
* Learn to plot a spectrum from Hubble
## Introduction
Quasars are extremely luminous astronomical objects that can be found at the center of some galaxies. They are powered by gas spiraling at high velocity into a super-massive black hole. The brightest quasars are capable of outshining all the stars in their galaxy; they can be seen from billions of light-years away. The first quasar ever discovered is called **3c273**. It is one of the most luminous quasars and therefore one of the most luminous objects in the observable universe. It is at a distance of 749 Megaparsecs [1 Megaparsec = 1 million parsecs = 3.26 million lightyears] with an absolute magnitude of &minus;26.7, meaning that if it were at a distance of 10 parsecs, it would be as bright in our sky as the Sun.
Quasar **3c273** is the our target in this tutorial. We will first search the MAST Archive for all the observations of this quasar. Then, we will display those observations in a historic coverage plot; that is, a plot of the wavelengths in which **3c273** was observed for a given year. This will help us understand what the history of observations of this quasar looks over time. Finally, we will plot a spectrum from one of the observations.
### Workflow
* [Imports](#Imports)
* [Historic Observation Coverage](#Historical-Observation-Coverage)
* [Query the MAST Archive](#Query-the-MAST-Archive-by-Object-Name)
* [Create Plotting Variables](#Create-Plotting-Variables)
* [Time Data](#Time-Data)
* [Wavelength Data](#Wavelength-Data)
* [Mission Names](#Mission-Names)
* [Plotting Historical Observation Coverage](#Plotting-Historical-Observation-Coverage)
* [Plot a Spectrum](#Plot-a-Spectrum)
* [Query HST for Spectra](#Query-HST-for-Spectra)
* [List Available Instrument and Filter Combinations](#List-Available-Instrument-and-Filter-Combinations)
* [Select Desired Observations](#Select-Observations-with-a-Specific-Instrument-and-Filter-Combination)
* [Filter for Relevant Products](#Filtering-for-Relevant-Products)
* [Download the Data](#Download-the-Data-to-Plot)
* [Plot the Spectrum](#Plot-the-Spectrum)
* [Exercises](#Exercises)</textarea>
<div aria-labelledby="nb-source-label" role="group">
<div class="highlight">
<pre><span></span><code><span class="gh" title="Generic.Heading"># Historical Quasar Observations</span>
***
<span class="gu" title="Generic.Subheading">## Learning Goals</span>
By the end of this tutorial, you will:
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>Understand how to query data on a target from the MAST archive.
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>Be able to plot a historic observation coverage plot of a target.
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>Learn to plot a spectrum from Hubble
<span class="gu" title="Generic.Subheading">## Introduction</span>
Quasars are extremely luminous astronomical objects that can be found at the center of some galaxies. They are powered by gas spiraling at high velocity into a super-massive black hole. The brightest quasars are capable of outshining all the stars in their galaxy; they can be seen from billions of light-years away. The first quasar ever discovered is called <span class="gs" title="Generic.Strong">**3c273**</span>. It is one of the most luminous quasars and therefore one of the most luminous objects in the observable universe. It is at a distance of 749 Megaparsecs [1 Megaparsec = 1 million parsecs = 3.26 million lightyears] with an absolute magnitude of &minus;26.7, meaning that if it were at a distance of 10 parsecs, it would be as bright in our sky as the Sun.
Quasar <span class="gs" title="Generic.Strong">**3c273**</span> is the our target in this tutorial. We will first search the MAST Archive for all the observations of this quasar. Then, we will display those observations in a historic coverage plot; that is, a plot of the wavelengths in which <span class="gs" title="Generic.Strong">**3c273**</span> was observed for a given year. This will help us understand what the history of observations of this quasar looks over time. Finally, we will plot a spectrum from one of the observations.
<span class="gu" title="Generic.Subheading">### Workflow</span>
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Imports</span>](<span class="na" title="Name.Attribute">#Imports</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Historic Observation Coverage</span>](<span class="na" title="Name.Attribute">#Historical-Observation-Coverage</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Query the MAST Archive</span>](<span class="na" title="Name.Attribute">#Query-the-MAST-Archive-by-Object-Name</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Create Plotting Variables</span>](<span class="na" title="Name.Attribute">#Create-Plotting-Variables</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Time Data</span>](<span class="na" title="Name.Attribute">#Time-Data</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Wavelength Data</span>](<span class="na" title="Name.Attribute">#Wavelength-Data</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Mission Names</span>](<span class="na" title="Name.Attribute">#Mission-Names</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Plotting Historical Observation Coverage</span>](<span class="na" title="Name.Attribute">#Plotting-Historical-Observation-Coverage</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Plot a Spectrum</span>](<span class="na" title="Name.Attribute">#Plot-a-Spectrum</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Query HST for Spectra</span>](<span class="na" title="Name.Attribute">#Query-HST-for-Spectra</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">List Available Instrument and Filter Combinations</span>](<span class="na" title="Name.Attribute">#List-Available-Instrument-and-Filter-Combinations</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Select Desired Observations</span>](<span class="na" title="Name.Attribute">#Select-Observations-with-a-Specific-Instrument-and-Filter-Combination</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Filter for Relevant Products</span>](<span class="na" title="Name.Attribute">#Filtering-for-Relevant-Products</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Download the Data</span>](<span class="na" title="Name.Attribute">#Download-the-Data-to-Plot</span>)
<span class="w" title="Text.Whitespace"> </span><span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Plot the Spectrum</span>](<span class="na" title="Name.Attribute">#Plot-the-Spectrum</span>)
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Exercises</span>](<span class="na" title="Name.Attribute">#Exercises</span>)
</code></pre>
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<h1>
<a href="#historical-quasar-observations">
Historical Quasar Observations
</a>
</h1>
<hr>
<h2>
<a href="#learning-goals">
Learning Goals
</a>
</h2>
<p>
By the end of this tutorial, you will:
</p>
<ul>
<li>
Understand how to query data on a target from the MAST archive.
</li>
<li>
Be able to plot a historic observation coverage plot of a target.
</li>
<li>
Learn to plot a spectrum from Hubble
</li>
</ul>
<h2>
<a href="#introduction">
Introduction
</a>
</h2>
<p>
Quasars are extremely luminous astronomical objects that can be found at the center of some galaxies. They are powered by gas spiraling at high velocity into a super-massive black hole. The brightest quasars are capable of outshining all the stars in their galaxy; they can be seen from billions of light-years away. The first quasar ever discovered is called
<strong>
3c273
</strong>
. It is one of the most luminous quasars and therefore one of the most luminous objects in the observable universe. It is at a distance of 749 Megaparsecs [1 Megaparsec = 1 million parsecs = 3.26 million lightyears] with an absolute magnitude of &minus;26.7, meaning that if it were at a distance of 10 parsecs, it would be as bright in our sky as the Sun.
</p>
<p>
Quasar
<strong>
3c273
</strong>
is the our target in this tutorial. We will first search the MAST Archive for all the observations of this quasar. Then, we will display those observations in a historic coverage plot; that is, a plot of the wavelengths in which
<strong>
3c273
</strong>
was observed for a given year. This will help us understand what the history of observations of this quasar looks over time. Finally, we will plot a spectrum from one of the observations.
</p>
<h3>
<a href="#workflow">
Workflow
</a>
</h3>
<ul>
<li>
<a href="#Imports">
Imports
</a>
</li>
<li>
<a href="#Historical-Observation-Coverage">
Historic Observation Coverage
</a>
<ul>
<li>
<a href="#Query-the-MAST-Archive-by-Object-Name">
Query the MAST Archive
</a>
</li>
<li>
<a href="#Create-Plotting-Variables">
Create Plotting Variables
</a>
<ul>
<li>
<a href="#Time-Data">
Time Data
</a>
</li>
<li>
<a href="#Wavelength-Data">
Wavelength Data
</a>
</li>
<li>
<a href="#Mission-Names">
Mission Names
</a>
</li>
</ul>
</li>
<li>
<a href="#Plotting-Historical-Observation-Coverage">
Plotting Historical Observation Coverage
</a>
</li>
</ul>
</li>
<li>
<a href="#Plot-a-Spectrum">
Plot a Spectrum
</a>
<ul>
<li>
<a href="#Query-HST-for-Spectra">
Query HST for Spectra
</a>
</li>
<li>
<a href="#List-Available-Instrument-and-Filter-Combinations">
List Available Instrument and Filter Combinations
</a>
</li>
<li>
<a href="#Select-Observations-with-a-Specific-Instrument-and-Filter-Combination">
Select Desired Observations
</a>
</li>
<li>
<a href="#Filtering-for-Relevant-Products">
Filter for Relevant Products
</a>
</li>
<li>
<a href="#Download-the-Data-to-Plot">
Download the Data
</a>
</li>
<li>
<a href="#Plot-the-Spectrum">
Plot the Spectrum
</a>
</li>
</ul>
</li>
<li>
<a href="#Exercises">
Exercises
</a>
</li>
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<textarea aria-labelledby="cell-2-source-label nb-source-label" class="nb-source" form="cell-2" id="cell-2-source" name="source" readonly rows="9">## Imports
The following cell holds the imported packages. These packages are necessary for running the rest of the cells in this notebook. A description of each import is as follows:
* `numpy` to handle array functions
* `pandas` to handle date conversions
* `fits` from astropy.io for accessing FITS files
* `Table` from astropy.table for creating tidy tables of the data
* `matplotlib.pyplot` for plotting data
* `Mast` and `Observations` from astroquery.mast for querying data and observations from the MAST archive</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Imports</span>
The following cell holds the imported packages. These packages are necessary for running the rest of the cells in this notebook. A description of each import is as follows:
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`numpy`</span> to handle array functions
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`pandas`</span> to handle date conversions
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`fits`</span> from astropy.io for accessing FITS files
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`Table`</span> from astropy.table for creating tidy tables of the data
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`matplotlib.pyplot`</span> for plotting data
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span><span class="sb" title="Literal.String.Backtick">`Mast`</span> and <span class="sb" title="Literal.String.Backtick">`Observations`</span> from astroquery.mast for querying data and observations from the MAST archive
</code></pre>
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<h2>
<a href="#imports">
Imports
</a>
</h2>
<p>
The following cell holds the imported packages. These packages are necessary for running the rest of the cells in this notebook. A description of each import is as follows:
</p>
<ul>
<li>
<code>
numpy
</code>
to handle array functions
</li>
<li>
<code>
pandas
</code>
to handle date conversions
</li>
<li>
<code>
fits
</code>
from
<a href="http://astropy.io">
astropy.io
</a>
for accessing FITS files
</li>
<li>
<code>
Table
</code>
from astropy.table for creating tidy tables of the data
</li>
<li>
<code>
matplotlib.pyplot
</code>
for plotting data
</li>
<li>
<code>
Mast
</code>
and
<code>
Observations
</code>
from astroquery.mast for querying data and observations from the MAST archive
</li>
</ul>
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<textarea aria-labelledby="cell-3-source-label nb-source-label" class="nb-source" form="cell-3" id="cell-3-source" name="source" readonly rows="10">from astropy.io import fits
from astropy.table import Table
from astroquery.mast import Mast, Observations
import itertools
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline</textarea>
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<pre><span></span><code><span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astropy.io</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">fits</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astropy.table</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">Table</span>
<span class="kn" title="Keyword.Namespace">from</span> <span class="nn" title="Name.Namespace">astroquery.mast</span> <span class="kn" title="Keyword.Namespace">import</span> <span class="n" title="Name">Mast</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">Observations</span>
<span class="kn" title="Keyword.Namespace">import</span> <span class="nn" title="Name.Namespace">itertools</span>
<span class="kn" title="Keyword.Namespace">import</span> <span class="nn" title="Name.Namespace">numpy</span> <span class="k" title="Keyword">as</span> <span class="nn" title="Name.Namespace">np</span>
<span class="kn" title="Keyword.Namespace">import</span> <span class="nn" title="Name.Namespace">pandas</span> <span class="k" title="Keyword">as</span> <span class="nn" title="Name.Namespace">pd</span>
<span class="kn" title="Keyword.Namespace">import</span> <span class="nn" title="Name.Namespace">matplotlib.pyplot</span> <span class="k" title="Keyword">as</span> <span class="nn" title="Name.Namespace">plt</span>
<span class="o" title="Operator">%</span><span class="n" title="Name">matplotlib</span> <span class="n" title="Name">inline</span>
</code></pre>
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<textarea aria-labelledby="cell-4-source-label nb-source-label" class="nb-source" form="cell-4" id="cell-4-source" name="source" readonly rows="2"># Historical Observation Coverage
The MAST Archive has data from as early as the 1970s. In this section, we'll search for a target by name and examine its observational history. We'll create a plot of this history, showing when the target was observed, in what wavelength, and by which mission.</textarea>
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<pre><span></span><code><span class="gh" title="Generic.Heading"># Historical Observation Coverage</span>
The MAST Archive has data from as early as the 1970s. In this section, we'll search for a target by name and examine its observational history. We'll create a plot of this history, showing when the target was observed, in what wavelength, and by which mission.
</code></pre>
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<h1>
<a href="#historical-observation-coverage">
Historical Observation Coverage
</a>
</h1>
<p>
The MAST Archive has data from as early as the 1970s. In this section, we'll search for a target by name and examine its observational history. We'll create a plot of this history, showing when the target was observed, in what wavelength, and by which mission.
</p>
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<textarea aria-labelledby="cell-5-source-label nb-source-label" class="nb-source" form="cell-5" id="cell-5-source" name="source" readonly rows="3">## Query the MAST Archive by Object Name
We are going to use the **astroquery.mast** `Observations` package to gather our data from the MAST Archive. In this tutorial, we will use the `Observations.query_object()` function which takes the name of the target and an optional radius. If you don't specify a radius, 0.2 degrees will be used by default. For more information about queries you can read the [astroquery.mast readthedocs](https://astroquery.readthedocs.io/en/latest/mast/mast.html).</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Query the MAST Archive by Object Name</span>
We are going to use the <span class="gs" title="Generic.Strong">**astroquery.mast**</span> <span class="sb" title="Literal.String.Backtick">`Observations`</span> package to gather our data from the MAST Archive. In this tutorial, we will use the <span class="sb" title="Literal.String.Backtick">`Observations.query_object()`</span> function which takes the name of the target and an optional radius. If you don't specify a radius, 0.2 degrees will be used by default. For more information about queries you can read the [<span class="nt" title="Name.Tag">astroquery.mast readthedocs</span>](<span class="na" title="Name.Attribute">https://astroquery.readthedocs.io/en/latest/mast/mast.html</span>).
</code></pre>
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<h2>
<a href="#query-the-mast-archive-by-object-name">
Query the MAST Archive by Object Name
</a>
</h2>
<p>
We are going to use the
<strong>
astroquery.mast
</strong>
<code>
Observations
</code>
package to gather our data from the MAST Archive. In this tutorial, we will use the
<code>
Observations.query_object()
</code>
function which takes the name of the target and an optional radius. If you don't specify a radius, 0.2 degrees will be used by default. For more information about queries you can read the
<a href="https://astroquery.readthedocs.io/en/latest/mast/mast.html">
astroquery.mast readthedocs
</a>
.
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<textarea aria-labelledby="cell-6-source-label nb-source-label" class="nb-source" form="cell-6" id="cell-6-source" name="source" readonly rows="5"># Define target name
target_name = "3c273"
# We'll name the data "obs_table" for "observations table"
obs_table = Observations.query_object(target_name)</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Define target name</span>
<span class="n" title="Name">target_name</span> <span class="o" title="Operator">=</span> <span class="s2" title="Literal.String.Double">"3c273"</span>
<span class="c1" title="Comment.Single"># We'll name the data "obs_table" for "observations table"</span>
<span class="n" title="Name">obs_table</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">query_object</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">target_name</span><span class="p" title="Punctuation">)</span>
</code></pre>
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<textarea aria-labelledby="cell-7-source-label nb-source-label" class="nb-source" form="cell-7" id="cell-7-source" name="source" readonly rows="3"># Print out the first 10 entries with limited columns, if you want to see a preview
columns = ['intentType', 'obs_collection', 'wavelength_region', 'target_name', 'dataproduct_type', 'em_min']
obs_table[:10][columns].show_in_notebook()</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Print out the first 10 entries with limited columns, if you want to see a preview</span>
<span class="n" title="Name">columns</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'intentType'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'obs_collection'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'wavelength_region'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'target_name'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'dataproduct_type'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'em_min'</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">obs_table</span><span class="p" title="Punctuation">[:</span><span class="mi" title="Literal.Number.Integer">10</span><span class="p" title="Punctuation">][</span><span class="n" title="Name">columns</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">show_in_notebook</span><span class="p" title="Punctuation">()</span>
</code></pre>
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<textarea aria-labelledby="cell-8-source-label nb-source-label" class="nb-source" form="cell-8" id="cell-8-source" name="source" readonly rows="12">## Create Plotting Variables
Before we start parsing our observations table, let's recall what we want to do with it.
First, we want to plot a historical observation coverage plot, where the horizontal axis will be time and the vertical axis will be observed wavelength. We should label or color each observation according to what mission it corresponds to. So, we are going need variables for:
* array of times of all observations = `times`
* array of wavelengths of all observations = `waves`
* array of mission names of all observations = `mission` &lt;br&gt;
We will want to modify the queried data for easy visualization:
1) We will want to convert the Modified Julian Date (MJD) to a calendar year so when we plot the timeline, it will be easy to tell when each observation was made.
2) MAST only holds columns for the minimum and maximum wavelength of the observation, but for the historic observation coverage plot, we only want one wavelength per observation. We will calculate the average of the min/max values, and use this number instead.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Create Plotting Variables</span>
Before we start parsing our observations table, let's recall what we want to do with it.
First, we want to plot a historical observation coverage plot, where the horizontal axis will be time and the vertical axis will be observed wavelength. We should label or color each observation according to what mission it corresponds to. So, we are going need variables for:
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>array of times of all observations = <span class="sb" title="Literal.String.Backtick">`times`</span>
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>array of wavelengths of all observations = <span class="sb" title="Literal.String.Backtick">`waves`</span>
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>array of mission names of all observations = <span class="sb" title="Literal.String.Backtick">`mission`</span> &lt;br&gt;
We will want to modify the queried data for easy visualization:
1) We will want to convert the Modified Julian Date (MJD) to a calendar year so when we plot the timeline, it will be easy to tell when each observation was made.
2) MAST only holds columns for the minimum and maximum wavelength of the observation, but for the historic observation coverage plot, we only want one wavelength per observation. We will calculate the average of the min/max values, and use this number instead.
</code></pre>
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<h2>
<a href="#create-plotting-variables">
Create Plotting Variables
</a>
</h2>
<p>
Before we start parsing our observations table, let's recall what we want to do with it.
</p>
<p>
First, we want to plot a historical observation coverage plot, where the horizontal axis will be time and the vertical axis will be observed wavelength. We should label or color each observation according to what mission it corresponds to. So, we are going need variables for:
</p>
<ul>
<li>
array of times of all observations =
<code>
times
</code>
</li>
<li>
array of wavelengths of all observations =
<code>
waves
</code>
</li>
<li>
array of mission names of all observations =
<code>
mission
</code>
<br>
</li>
</ul>
<p>
We will want to modify the queried data for easy visualization:
</p>
<ol>
<li>
<p>
We will want to convert the Modified Julian Date (MJD) to a calendar year so when we plot the timeline, it will be easy to tell when each observation was made.
</p>
</li>
<li>
<p>
MAST only holds columns for the minimum and maximum wavelength of the observation, but for the historic observation coverage plot, we only want one wavelength per observation. We will calculate the average of the min/max values, and use this number instead.
</p>
</li>
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<textarea aria-labelledby="cell-9-source-label nb-source-label" class="nb-source" form="cell-9" id="cell-9-source" name="source" readonly rows="2">### Time Data
We'll use `t_min` for our time data, which corresponds to the beginning of the observation. This is "good enough" for our plot, since it will span multiple decades.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### Time Data</span>
We'll use <span class="sb" title="Literal.String.Backtick">`t_min`</span> for our time data, which corresponds to the beginning of the observation. This is "good enough" for our plot, since it will span multiple decades.
</code></pre>
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<h3>
<a href="#time-data">
Time Data
</a>
</h3>
<p>
We'll use
<code>
t_min
</code>
for our time data, which corresponds to the beginning of the observation. This is "good enough" for our plot, since it will span multiple decades.
</p>
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<textarea aria-labelledby="cell-10-source-label nb-source-label" class="nb-source" form="cell-10" id="cell-10-source" name="source" readonly rows="17"># Parse the observations table to get the Time data
obs_times = obs_table["t_min"]
# Convert MJD to Calendar Date:
# Initialize list for times as calendar dates
times = []
# Loop through times queried from MAST
for t in obs_times:
# Convert MJD to Julian date
t = t + 2400000.5
# Convert Julian date to Calendar date
time = pd.to_datetime(t, unit = 'D', origin = 'julian')
# Add converted date to times list
times.append(time.to_numpy())
# Change list to numpy array for easy plotting
times = np.array(times)</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Parse the observations table to get the Time data</span>
<span class="n" title="Name">obs_times</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">obs_table</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"t_min"</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># Convert MJD to Calendar Date:</span>
<span class="c1" title="Comment.Single"># Initialize list for times as calendar dates</span>
<span class="n" title="Name">times</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[]</span>
<span class="c1" title="Comment.Single"># Loop through times queried from MAST</span>
<span class="k" title="Keyword">for</span> <span class="n" title="Name">t</span> <span class="ow" title="Operator.Word">in</span> <span class="n" title="Name">obs_times</span><span class="p" title="Punctuation">:</span>
<span class="c1" title="Comment.Single"># Convert MJD to Julian date</span>
<span class="n" title="Name">t</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">t</span> <span class="o" title="Operator">+</span> <span class="mf" title="Literal.Number.Float">2400000.5</span>
<span class="c1" title="Comment.Single"># Convert Julian date to Calendar date </span>
<span class="n" title="Name">time</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">pd</span><span class="o" title="Operator">.</span><span class="n" title="Name">to_datetime</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">t</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">unit</span> <span class="o" title="Operator">=</span> <span class="s1" title="Literal.String.Single">'D'</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">origin</span> <span class="o" title="Operator">=</span> <span class="s1" title="Literal.String.Single">'julian'</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Add converted date to times list</span>
<span class="n" title="Name">times</span><span class="o" title="Operator">.</span><span class="n" title="Name">append</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">time</span><span class="o" title="Operator">.</span><span class="n" title="Name">to_numpy</span><span class="p" title="Punctuation">())</span>
<span class="c1" title="Comment.Single"># Change list to numpy array for easy plotting</span>
<span class="n" title="Name">times</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">array</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">times</span><span class="p" title="Punctuation">)</span>
</code></pre>
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<textarea aria-labelledby="cell-11-source-label nb-source-label" class="nb-source" form="cell-11" id="cell-11-source" name="source" readonly rows="3">### Wavelength Data
Again, we'll compute the average value reported for the wavelength. One complication here is the presence of a database error in some legacy missions. The current standard is to report wavelengths in meters, but some older missions reported the value in nanometers. Work is ongoing to bring these legacy missions up date; the code in the cell below will correctly parse the values, whether or not the database has been fixed.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### Wavelength Data</span>
Again, we'll compute the average value reported for the wavelength. One complication here is the presence of a database error in some legacy missions. The current standard is to report wavelengths in meters, but some older missions reported the value in nanometers. Work is ongoing to bring these legacy missions up date; the code in the cell below will correctly parse the values, whether or not the database has been fixed.
</code></pre>
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<h3>
<a href="#wavelength-data">
Wavelength Data
</a>
</h3>
<p>
Again, we'll compute the average value reported for the wavelength. One complication here is the presence of a database error in some legacy missions. The current standard is to report wavelengths in meters, but some older missions reported the value in nanometers. Work is ongoing to bring these legacy missions up date; the code in the cell below will correctly parse the values, whether or not the database has been fixed.
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<textarea aria-labelledby="cell-12-source-label nb-source-label" class="nb-source" form="cell-12" id="cell-12-source" name="source" readonly rows="17"># Parse observations table to get the wavelength data
wavelength_min = obs_table["em_min"]
wavelength_max = obs_table["em_max"]
# Get the average wavelength
wavelength_avg = (wavelength_max+wavelength_min)/2
# Some older missions have wavelengths that are off by a factor of 10^9
waves = []
for wave in wavelength_avg:
if wave/1e9 &gt;= 1:
waves.append(wave/1e9)
else:
waves.append(wave)
#change list to numpy array
waves = np.array(waves)</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Parse observations table to get the wavelength data</span>
<span class="n" title="Name">wavelength_min</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">obs_table</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"em_min"</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">wavelength_max</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">obs_table</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"em_max"</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># Get the average wavelength</span>
<span class="n" title="Name">wavelength_avg</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">(</span><span class="n" title="Name">wavelength_max</span><span class="o" title="Operator">+</span><span class="n" title="Name">wavelength_min</span><span class="p" title="Punctuation">)</span><span class="o" title="Operator">/</span><span class="mi" title="Literal.Number.Integer">2</span>
<span class="c1" title="Comment.Single"># Some older missions have wavelengths that are off by a factor of 10^9</span>
<span class="n" title="Name">waves</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[]</span>
<span class="k" title="Keyword">for</span> <span class="n" title="Name">wave</span> <span class="ow" title="Operator.Word">in</span> <span class="n" title="Name">wavelength_avg</span><span class="p" title="Punctuation">:</span>
<span class="k" title="Keyword">if</span> <span class="n" title="Name">wave</span><span class="o" title="Operator">/</span><span class="mf" title="Literal.Number.Float">1e9</span> <span class="o" title="Operator">&gt;=</span> <span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">:</span>
<span class="n" title="Name">waves</span><span class="o" title="Operator">.</span><span class="n" title="Name">append</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">wave</span><span class="o" title="Operator">/</span><span class="mf" title="Literal.Number.Float">1e9</span><span class="p" title="Punctuation">)</span>
<span class="k" title="Keyword">else</span><span class="p" title="Punctuation">:</span>
<span class="n" title="Name">waves</span><span class="o" title="Operator">.</span><span class="n" title="Name">append</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">wave</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single">#change list to numpy array</span>
<span class="n" title="Name">waves</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">array</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">waves</span><span class="p" title="Punctuation">)</span>
</code></pre>
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<textarea aria-labelledby="cell-13-source-label nb-source-label" class="nb-source" form="cell-13" id="cell-13-source" name="source" readonly rows="2">### Mission Names
We'll use the mission names to create labels for the data. It will make our plot a little noisier, but it's good to know where the data is coming from.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### Mission Names</span>
We'll use the mission names to create labels for the data. It will make our plot a little noisier, but it's good to know where the data is coming from.
</code></pre>
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<h3>
<a href="#mission-names">
Mission Names
</a>
</h3>
<p>
We'll use the mission names to create labels for the data. It will make our plot a little noisier, but it's good to know where the data is coming from.
</p>
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<textarea aria-labelledby="cell-14-source-label nb-source-label" class="nb-source" form="cell-14" id="cell-14-source" name="source" readonly rows="3">#Parse the observations table to get the mission names data
mission = obs_table["obs_collection"]
mission = np.array(mission)</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single">#Parse the observations table to get the mission names data</span>
<span class="n" title="Name">mission</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">obs_table</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"obs_collection"</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">mission</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">array</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">mission</span><span class="p" title="Punctuation">)</span>
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<textarea aria-labelledby="cell-15-source-label nb-source-label" class="nb-source" form="cell-15" id="cell-15-source" name="source" readonly rows="3">## Plotting Historical Observation Coverage
We want to visualize the history of observations of **3c273** according to the wavelength of the observation. We'll change the color and shape of each point to indicate which mission made the observation. </textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">## Plotting Historical Observation Coverage</span>
We want to visualize the history of observations of <span class="gs" title="Generic.Strong">**3c273**</span> according to the wavelength of the observation. We'll change the color and shape of each point to indicate which mission made the observation.
</code></pre>
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<h2>
<a href="#plotting-historical-observation-coverage">
Plotting Historical Observation Coverage
</a>
</h2>
<p>
We want to visualize the history of observations of
<strong>
3c273
</strong>
according to the wavelength of the observation. We'll change the color and shape of each point to indicate which mission made the observation.
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<textarea aria-labelledby="cell-16-source-label nb-source-label" class="nb-source" form="cell-16" id="cell-16-source" name="source" readonly rows="28"># Initialize the figure
fig = plt.figure()
fig.set_size_inches(15,10)
ax = fig.add_subplot()
# Set a color for every unique mission name
cm = plt.cm.get_cmap("plasma")
num_colors = len(np.unique(mission))
ax.set_prop_cycle(color = [cm(1.*i/num_colors) for i in range(num_colors)])
marker = itertools.cycle(('^', 'o', 's','*'))
# Loop through the mission names
for i in np.unique(mission):
# Filter times and wavelengths by mission name
ind = np.where(mission == i)
# Plot the mission in its color as a scatterplot
ax.scatter(times[ind], waves[ind], label = i, s = 100, marker = next(marker))
# Place the legend
plt.legend()
# Set the label of the x and y axes
plt.xlabel("Time of Observation [Year]", fontsize = 15)
plt.ylabel("Wavelength of Observation [nm]", fontsize = 15)
# Show the plot
plt.show()</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Initialize the figure</span>
<span class="n" title="Name">fig</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">figure</span><span class="p" title="Punctuation">()</span>
<span class="n" title="Name">fig</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_size_inches</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">10</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ax</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">fig</span><span class="o" title="Operator">.</span><span class="n" title="Name">add_subplot</span><span class="p" title="Punctuation">()</span>
<span class="c1" title="Comment.Single"># Set a color for every unique mission name</span>
<span class="n" title="Name">cm</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">cm</span><span class="o" title="Operator">.</span><span class="n" title="Name">get_cmap</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"plasma"</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">num_colors</span> <span class="o" title="Operator">=</span> <span class="nb" title="Name.Builtin">len</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">unique</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">mission</span><span class="p" title="Punctuation">))</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_prop_cycle</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">color</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="n" title="Name">cm</span><span class="p" title="Punctuation">(</span><span class="mf" title="Literal.Number.Float">1.</span><span class="o" title="Operator">*</span><span class="n" title="Name">i</span><span class="o" title="Operator">/</span><span class="n" title="Name">num_colors</span><span class="p" title="Punctuation">)</span> <span class="k" title="Keyword">for</span> <span class="n" title="Name">i</span> <span class="ow" title="Operator.Word">in</span> <span class="nb" title="Name.Builtin">range</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">num_colors</span><span class="p" title="Punctuation">)])</span>
<span class="n" title="Name">marker</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">itertools</span><span class="o" title="Operator">.</span><span class="n" title="Name">cycle</span><span class="p" title="Punctuation">((</span><span class="s1" title="Literal.String.Single">'^'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'o'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'s'</span><span class="p" title="Punctuation">,</span><span class="s1" title="Literal.String.Single">'*'</span><span class="p" title="Punctuation">))</span>
<span class="c1" title="Comment.Single"># Loop through the mission names</span>
<span class="k" title="Keyword">for</span> <span class="n" title="Name">i</span> <span class="ow" title="Operator.Word">in</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">unique</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">mission</span><span class="p" title="Punctuation">):</span>
<span class="c1" title="Comment.Single"># Filter times and wavelengths by mission name</span>
<span class="n" title="Name">ind</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">where</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">mission</span> <span class="o" title="Operator">==</span> <span class="n" title="Name">i</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Plot the mission in its color as a scatterplot</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">scatter</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">times</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">ind</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">waves</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">ind</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">label</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">i</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">s</span> <span class="o" title="Operator">=</span> <span class="mi" title="Literal.Number.Integer">100</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">marker</span> <span class="o" title="Operator">=</span> <span class="nb" title="Name.Builtin">next</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">marker</span><span class="p" title="Punctuation">))</span>
<span class="c1" title="Comment.Single"># Place the legend</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">legend</span><span class="p" title="Punctuation">()</span>
<span class="c1" title="Comment.Single"># Set the label of the x and y axes</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">xlabel</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"Time of Observation [Year]"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">fontsize</span> <span class="o" title="Operator">=</span> <span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">ylabel</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"Wavelength of Observation [nm]"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">fontsize</span> <span class="o" title="Operator">=</span> <span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Show the plot</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">show</span><span class="p" title="Punctuation">()</span>
</code></pre>
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<textarea aria-labelledby="cell-17-source-label nb-source-label" class="nb-source" form="cell-17" id="cell-17-source" name="source" readonly rows="1">Whoops! Looks like our results are dominated by Spitzer; let's drop that mission so we can see the other results.</textarea>
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<pre><span></span><code>Whoops! Looks like our results are dominated by Spitzer; let's drop that mission so we can see the other results.
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Whoops! Looks like our results are dominated by Spitzer; let's drop that mission so we can see the other results.
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<textarea aria-labelledby="cell-18-source-label nb-source-label" class="nb-source" form="cell-18" id="cell-18-source" name="source" readonly rows="29"># Initialize the figure
fig = plt.figure()
fig.set_size_inches(15,10)
ax = fig.add_subplot()
# Set a color for every unique mission name
cm = plt.cm.get_cmap("plasma")
num_colors = len(np.unique(mission))
ax.set_prop_cycle(color = [cm(1.*i/num_colors) for i in range(num_colors)])
marker = itertools.cycle(('^', 'o', 's','*'))
# Loop through the mission names
for i in np.unique(mission):
# Filter times and wavelengths by mission name
ind = np.where(mission == i)
# Plot the mission in its color as a scatterplot
if i != "SPITZER_SHA":
ax.scatter(times[ind], waves[ind], label = i, s = 100, marker = next(marker))
# Place the legend
plt.legend()
# Set the label of the x and y axes
plt.xlabel("Time of Observation [Year]", fontsize = 15)
plt.ylabel("Wavelength of Observation [nm]", fontsize = 15)
# Show the plot
plt.show()</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Initialize the figure</span>
<span class="n" title="Name">fig</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">figure</span><span class="p" title="Punctuation">()</span>
<span class="n" title="Name">fig</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_size_inches</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">10</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">ax</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">fig</span><span class="o" title="Operator">.</span><span class="n" title="Name">add_subplot</span><span class="p" title="Punctuation">()</span>
<span class="c1" title="Comment.Single"># Set a color for every unique mission name</span>
<span class="n" title="Name">cm</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">cm</span><span class="o" title="Operator">.</span><span class="n" title="Name">get_cmap</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"plasma"</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">num_colors</span> <span class="o" title="Operator">=</span> <span class="nb" title="Name.Builtin">len</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">unique</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">mission</span><span class="p" title="Punctuation">))</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">set_prop_cycle</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">color</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="n" title="Name">cm</span><span class="p" title="Punctuation">(</span><span class="mf" title="Literal.Number.Float">1.</span><span class="o" title="Operator">*</span><span class="n" title="Name">i</span><span class="o" title="Operator">/</span><span class="n" title="Name">num_colors</span><span class="p" title="Punctuation">)</span> <span class="k" title="Keyword">for</span> <span class="n" title="Name">i</span> <span class="ow" title="Operator.Word">in</span> <span class="nb" title="Name.Builtin">range</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">num_colors</span><span class="p" title="Punctuation">)])</span>
<span class="n" title="Name">marker</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">itertools</span><span class="o" title="Operator">.</span><span class="n" title="Name">cycle</span><span class="p" title="Punctuation">((</span><span class="s1" title="Literal.String.Single">'^'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'o'</span><span class="p" title="Punctuation">,</span> <span class="s1" title="Literal.String.Single">'s'</span><span class="p" title="Punctuation">,</span><span class="s1" title="Literal.String.Single">'*'</span><span class="p" title="Punctuation">))</span>
<span class="c1" title="Comment.Single"># Loop through the mission names</span>
<span class="k" title="Keyword">for</span> <span class="n" title="Name">i</span> <span class="ow" title="Operator.Word">in</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">unique</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">mission</span><span class="p" title="Punctuation">):</span>
<span class="c1" title="Comment.Single"># Filter times and wavelengths by mission name</span>
<span class="n" title="Name">ind</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">where</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">mission</span> <span class="o" title="Operator">==</span> <span class="n" title="Name">i</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Plot the mission in its color as a scatterplot</span>
<span class="k" title="Keyword">if</span> <span class="n" title="Name">i</span> <span class="o" title="Operator">!=</span> <span class="s2" title="Literal.String.Double">"SPITZER_SHA"</span><span class="p" title="Punctuation">:</span>
<span class="n" title="Name">ax</span><span class="o" title="Operator">.</span><span class="n" title="Name">scatter</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">times</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">ind</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">waves</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">ind</span><span class="p" title="Punctuation">],</span> <span class="n" title="Name">label</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">i</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">s</span> <span class="o" title="Operator">=</span> <span class="mi" title="Literal.Number.Integer">100</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">marker</span> <span class="o" title="Operator">=</span> <span class="nb" title="Name.Builtin">next</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">marker</span><span class="p" title="Punctuation">))</span>
<span class="c1" title="Comment.Single"># Place the legend</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">legend</span><span class="p" title="Punctuation">()</span>
<span class="c1" title="Comment.Single"># Set the label of the x and y axes</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">xlabel</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"Time of Observation [Year]"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">fontsize</span> <span class="o" title="Operator">=</span> <span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">ylabel</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"Wavelength of Observation [nm]"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">fontsize</span> <span class="o" title="Operator">=</span> <span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Show the plot</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">show</span><span class="p" title="Punctuation">()</span>
</code></pre>
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<textarea aria-labelledby="cell-19-source-label nb-source-label" class="nb-source" form="cell-19" id="cell-19-source" name="source" readonly rows="1">This gives us a much better view of the wavelength coverage. We have good coverage of wavelengths from the mid-90s, thanks to Hubble. Let's take a look at some of this data and create a spectrum.</textarea>
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<pre><span></span><code>This gives us a much better view of the wavelength coverage. We have good coverage of wavelengths from the mid-90s, thanks to Hubble. Let's take a look at some of this data and create a spectrum.
</code></pre>
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<p>
This gives us a much better view of the wavelength coverage. We have good coverage of wavelengths from the mid-90s, thanks to Hubble. Let's take a look at some of this data and create a spectrum.
</p>
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<textarea aria-labelledby="cell-20-source-label nb-source-label" class="nb-source" form="cell-20" id="cell-20-source" name="source" readonly rows="7"># Plot a Spectrum
Now, we want to plot a spectrum from one of our observations, where the x-axis will be wavelength and the y-axis will be flux (brightness).
We can use our historical observational coverage plot to choose which observation to plot. Let's pick one from the Hubble Space Telescope (HST). Its high resolution coverage of the ultraviolet makes emission and absorption lines in this region clear; this can help us deduce the composition of **3c273**.
### Query HST for Spectra</textarea>
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<pre><span></span><code><span class="gh" title="Generic.Heading"># Plot a Spectrum</span>
Now, we want to plot a spectrum from one of our observations, where the x-axis will be wavelength and the y-axis will be flux (brightness).
We can use our historical observational coverage plot to choose which observation to plot. Let's pick one from the Hubble Space Telescope (HST). Its high resolution coverage of the ultraviolet makes emission and absorption lines in this region clear; this can help us deduce the composition of <span class="gs" title="Generic.Strong">**3c273**</span>.
<span class="gu" title="Generic.Subheading">### Query HST for Spectra</span>
</code></pre>
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<h1>
<a href="#plot-a-spectrum">
Plot a Spectrum
</a>
</h1>
<p>
Now, we want to plot a spectrum from one of our observations, where the x-axis will be wavelength and the y-axis will be flux (brightness).
</p>
<p>
We can use our historical observational coverage plot to choose which observation to plot. Let's pick one from the Hubble Space Telescope (HST). Its high resolution coverage of the ultraviolet makes emission and absorption lines in this region clear; this can help us deduce the composition of
<strong>
3c273
</strong>
.
</p>
<h3>
<a href="#query-hst-for-spectra">
Query HST for Spectra
</a>
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<textarea aria-labelledby="cell-21-source-label nb-source-label" class="nb-source" form="cell-21" id="cell-21-source" name="source" readonly rows="7"># Query all the observations of 3c273 from the Hubble Space Telescope
hst_table = Observations.query_criteria(objectname="3c273",radius="10 arcsec",
dataproduct_type="spectrum", obs_collection="HST")
# Let's print out some relevant columns of this table
columns = ["instrument_name","filters","target_name","obs_id","calib_level","t_exptime"]
hst_table[columns][:10]</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Query all the observations of 3c273 from the Hubble Space Telescope</span>
<span class="n" title="Name">hst_table</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">query_criteria</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">objectname</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"3c273"</span><span class="p" title="Punctuation">,</span><span class="n" title="Name">radius</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"10 arcsec"</span><span class="p" title="Punctuation">,</span>
<span class="n" title="Name">dataproduct_type</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"spectrum"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">obs_collection</span><span class="o" title="Operator">=</span><span class="s2" title="Literal.String.Double">"HST"</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Let's print out some relevant columns of this table</span>
<span class="n" title="Name">columns</span> <span class="o" title="Operator">=</span> <span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"instrument_name"</span><span class="p" title="Punctuation">,</span><span class="s2" title="Literal.String.Double">"filters"</span><span class="p" title="Punctuation">,</span><span class="s2" title="Literal.String.Double">"target_name"</span><span class="p" title="Punctuation">,</span><span class="s2" title="Literal.String.Double">"obs_id"</span><span class="p" title="Punctuation">,</span><span class="s2" title="Literal.String.Double">"calib_level"</span><span class="p" title="Punctuation">,</span><span class="s2" title="Literal.String.Double">"t_exptime"</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">hst_table</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">columns</span><span class="p" title="Punctuation">][:</span><span class="mi" title="Literal.Number.Integer">10</span><span class="p" title="Punctuation">]</span>
</code></pre>
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<textarea aria-labelledby="cell-22-source-label nb-source-label" class="nb-source" form="cell-22" id="cell-22-source" name="source" readonly rows="5">### List Available Instrument and Filter Combinations
Most telescopes will have multiple instruments and observing modes. Here we'll print a summary of the filters and instruments that are available for our search results. This is useful to us because the different instruments aboard Hubble will cover different wavelength ranges.
In our table, we'll also create two new columns: average exposure time and maximum exposure time. This can help constrain searches for faint objects, or targets that need a longer exposure to be fully resolved.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### List Available Instrument and Filter Combinations</span>
Most telescopes will have multiple instruments and observing modes. Here we'll print a summary of the filters and instruments that are available for our search results. This is useful to us because the different instruments aboard Hubble will cover different wavelength ranges.
In our table, we'll also create two new columns: average exposure time and maximum exposure time. This can help constrain searches for faint objects, or targets that need a longer exposure to be fully resolved.
</code></pre>
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<h3>
<a href="#list-available-instrument-and-filter-combinations">
List Available Instrument and Filter Combinations
</a>
</h3>
<p>
Most telescopes will have multiple instruments and observing modes. Here we'll print a summary of the filters and instruments that are available for our search results. This is useful to us because the different instruments aboard Hubble will cover different wavelength ranges.
</p>
<p>
In our table, we'll also create two new columns: average exposure time and maximum exposure time. This can help constrain searches for faint objects, or targets that need a longer exposure to be fully resolved.
</p>
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<textarea aria-labelledby="cell-23-source-label nb-source-label" class="nb-source" form="cell-23" id="cell-23-source" name="source" readonly rows="12">hst_table['count'] = 1
columns = hst_table.group_by(["instrument_name","filters"])
summary_table = columns["instrument_name","filters","count"].groups.aggregate(np.sum)
# Create two new columns: the average exposure time, and the maximum
summary_table["avg_exptime"] = columns['t_exptime'].groups.aggregate(np.mean)
summary_table["max_exptime"] = columns['t_exptime'].groups.aggregate(np.max)
summary_table["avg_exptime"].format = ".1f"
summary_table["max_exptime"].format = ".1f"
#Take a look at the summary table
summary_table</textarea>
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<pre><span></span><code><span class="n" title="Name">hst_table</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'count'</span><span class="p" title="Punctuation">]</span> <span class="o" title="Operator">=</span> <span class="mi" title="Literal.Number.Integer">1</span>
<span class="n" title="Name">columns</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">hst_table</span><span class="o" title="Operator">.</span><span class="n" title="Name">group_by</span><span class="p" title="Punctuation">([</span><span class="s2" title="Literal.String.Double">"instrument_name"</span><span class="p" title="Punctuation">,</span><span class="s2" title="Literal.String.Double">"filters"</span><span class="p" title="Punctuation">])</span>
<span class="n" title="Name">summary_table</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">columns</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"instrument_name"</span><span class="p" title="Punctuation">,</span><span class="s2" title="Literal.String.Double">"filters"</span><span class="p" title="Punctuation">,</span><span class="s2" title="Literal.String.Double">"count"</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">groups</span><span class="o" title="Operator">.</span><span class="n" title="Name">aggregate</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">sum</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Create two new columns: the average exposure time, and the maximum</span>
<span class="n" title="Name">summary_table</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"avg_exptime"</span><span class="p" title="Punctuation">]</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">columns</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'t_exptime'</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">groups</span><span class="o" title="Operator">.</span><span class="n" title="Name">aggregate</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">mean</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">summary_table</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"max_exptime"</span><span class="p" title="Punctuation">]</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">columns</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'t_exptime'</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">groups</span><span class="o" title="Operator">.</span><span class="n" title="Name">aggregate</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">max</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">summary_table</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"avg_exptime"</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">format</span> <span class="o" title="Operator">=</span> <span class="s2" title="Literal.String.Double">".1f"</span>
<span class="n" title="Name">summary_table</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"max_exptime"</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">format</span> <span class="o" title="Operator">=</span> <span class="s2" title="Literal.String.Double">".1f"</span>
<span class="c1" title="Comment.Single">#Take a look at the summary table</span>
<span class="n" title="Name">summary_table</span>
</code></pre>
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<textarea aria-labelledby="cell-24-source-label nb-source-label" class="nb-source" form="cell-24" id="cell-24-source" name="source" readonly rows="3">### Select Observations with a Specific Instrument and Filter Combination
We are interested in an ultraviolet observation that has an appropriate number of observations. Many of these instrument and filter combinations only have 1 or 2 observations. The COS G130M data looks like a good possibility. Let's look at the observations for that mode. </textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### Select Observations with a Specific Instrument and Filter Combination</span>
We are interested in an ultraviolet observation that has an appropriate number of observations. Many of these instrument and filter combinations only have 1 or 2 observations. The COS G130M data looks like a good possibility. Let's look at the observations for that mode.
</code></pre>
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<h3>
<a href="#select-observations-with-a-specific-instrument-and-filter-combination">
Select Observations with a Specific Instrument and Filter Combination
</a>
</h3>
<p>
We are interested in an ultraviolet observation that has an appropriate number of observations. Many of these instrument and filter combinations only have 1 or 2 observations. The COS G130M data looks like a good possibility. Let's look at the observations for that mode.
</p>
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<textarea aria-labelledby="cell-25-source-label nb-source-label" class="nb-source" form="cell-25" id="cell-25-source" name="source" readonly rows="5">g130m_table = hst_table['obsid','obs_id','target_name','calib_level',
't_exptime','filters','em_min','em_max'][hst_table['filters']=='G130M']
# Print out the table of data for this specific filter configuration
g130m_table</textarea>
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<pre><span></span><code><span class="n" title="Name">g130m_table</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">hst_table</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'obsid'</span><span class="p" title="Punctuation">,</span><span class="s1" title="Literal.String.Single">'obs_id'</span><span class="p" title="Punctuation">,</span><span class="s1" title="Literal.String.Single">'target_name'</span><span class="p" title="Punctuation">,</span><span class="s1" title="Literal.String.Single">'calib_level'</span><span class="p" title="Punctuation">,</span>
<span class="s1" title="Literal.String.Single">'t_exptime'</span><span class="p" title="Punctuation">,</span><span class="s1" title="Literal.String.Single">'filters'</span><span class="p" title="Punctuation">,</span><span class="s1" title="Literal.String.Single">'em_min'</span><span class="p" title="Punctuation">,</span><span class="s1" title="Literal.String.Single">'em_max'</span><span class="p" title="Punctuation">][</span><span class="n" title="Name">hst_table</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'filters'</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">==</span><span class="s1" title="Literal.String.Single">'G130M'</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># Print out the table of data for this specific filter configuration</span>
<span class="n" title="Name">g130m_table</span>
</code></pre>
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<textarea aria-labelledby="cell-26-source-label nb-source-label" class="nb-source" form="cell-26" id="cell-26-source" name="source" readonly rows="2"># We'll take the longest exposure in this filter and plot the spectrum
sel_table = g130m_table[np.argmax(g130m_table['t_exptime'])]</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># We'll take the longest exposure in this filter and plot the spectrum</span>
<span class="n" title="Name">sel_table</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">g130m_table</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">argmax</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">g130m_table</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'t_exptime'</span><span class="p" title="Punctuation">])]</span>
</code></pre>
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<textarea aria-labelledby="cell-27-source-label nb-source-label" class="nb-source" form="cell-27" id="cell-27-source" name="source" readonly rows="2">#Take a look at the selected observation's data table
sel_table</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single">#Take a look at the selected observation's data table</span>
<span class="n" title="Name">sel_table</span>
</code></pre>
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<textarea aria-labelledby="cell-28-source-label nb-source-label" class="nb-source" form="cell-28" id="cell-28-source" name="source" readonly rows="2">### Filtering for Relevant Products
Our table of selected observations includes not just the spectra but also RAW files, dark scans, bias scans, and others, all of which are used in the calibration of the data. These are not necessary for plotting the spectrum, so we'll filter them out by selecting only the Minimum Recommended Products which are marked as intended for science.</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### Filtering for Relevant Products</span>
Our table of selected observations includes not just the spectra but also RAW files, dark scans, bias scans, and others, all of which are used in the calibration of the data. These are not necessary for plotting the spectrum, so we'll filter them out by selecting only the Minimum Recommended Products which are marked as intended for science.
</code></pre>
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<h3>
<a href="#filtering-for-relevant-products">
Filtering for Relevant Products
</a>
</h3>
<p>
Our table of selected observations includes not just the spectra but also RAW files, dark scans, bias scans, and others, all of which are used in the calibration of the data. These are not necessary for plotting the spectrum, so we'll filter them out by selecting only the Minimum Recommended Products which are marked as intended for science.
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<textarea aria-labelledby="cell-29-source-label nb-source-label" class="nb-source" form="cell-29" id="cell-29-source" name="source" readonly rows="9"># Query the observations from MAST to get a list of products for our selected observation
data_products = Observations.get_product_list(sel_table)
# Get the minimum required products
filtered = Observations.filter_products(data_products, productType='SCIENCE',
productGroupDescription='Minimum Recommended Products')
# Let's take a look at the products available for our selected observation
filtered</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Query the observations from MAST to get a list of products for our selected observation</span>
<span class="n" title="Name">data_products</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">get_product_list</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">sel_table</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Get the minimum required products</span>
<span class="n" title="Name">filtered</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">filter_products</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">data_products</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">productType</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'SCIENCE'</span><span class="p" title="Punctuation">,</span>
<span class="n" title="Name">productGroupDescription</span><span class="o" title="Operator">=</span><span class="s1" title="Literal.String.Single">'Minimum Recommended Products'</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Let's take a look at the products available for our selected observation</span>
<span class="n" title="Name">filtered</span>
</code></pre>
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<textarea aria-labelledby="cell-30-source-label nb-source-label" class="nb-source" form="cell-30" id="cell-30-source" name="source" readonly rows="1">### Download the Data to Plot</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### Download the Data to Plot</span>
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<a href="#download-the-data-to-plot">
Download the Data to Plot
</a>
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<textarea aria-labelledby="cell-31-source-label nb-source-label" class="nb-source" form="cell-31" id="cell-31-source" name="source" readonly rows="2"># Download the data for our selected observation
data = Observations.download_products(filtered)</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Download the data for our selected observation</span>
<span class="n" title="Name">data</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Observations</span><span class="o" title="Operator">.</span><span class="n" title="Name">download_products</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">filtered</span><span class="p" title="Punctuation">)</span>
</code></pre>
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<textarea aria-labelledby="cell-32-source-label nb-source-label" class="nb-source" form="cell-32" id="cell-32-source" name="source" readonly rows="5">We've downloaded a [Flexible Image Transport System](https://fits.gsfc.nasa.gov/) (FITS) file. This is a very common file type used in astronomy for holding data of multiple dimensions. FITS files can hold images but can also contain spectral and temporal information.
We can read the columns of our FITS file to see that it holds two segements of data for this observation, FUVA and FUVB. These are two different subsections of the far-ultraviolet spectrum that Hubble observes.
To plot a spectrum, we'll need to get the data from the `Wavelength` and `Flux` columns. </textarea>
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<pre><span></span><code>We've downloaded a [<span class="nt" title="Name.Tag">Flexible Image Transport System</span>](<span class="na" title="Name.Attribute">https://fits.gsfc.nasa.gov/</span>) (FITS) file. This is a very common file type used in astronomy for holding data of multiple dimensions. FITS files can hold images but can also contain spectral and temporal information.
We can read the columns of our FITS file to see that it holds two segements of data for this observation, FUVA and FUVB. These are two different subsections of the far-ultraviolet spectrum that Hubble observes.
To plot a spectrum, we'll need to get the data from the <span class="sb" title="Literal.String.Backtick">`Wavelength`</span> and <span class="sb" title="Literal.String.Backtick">`Flux`</span> columns.
</code></pre>
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<p>
We've downloaded a
<a href="https://fits.gsfc.nasa.gov/">
Flexible Image Transport System
</a>
(FITS) file. This is a very common file type used in astronomy for holding data of multiple dimensions. FITS files can hold images but can also contain spectral and temporal information.
</p>
<p>
We can read the columns of our FITS file to see that it holds two segements of data for this observation, FUVA and FUVB. These are two different subsections of the far-ultraviolet spectrum that Hubble observes.
</p>
<p>
To plot a spectrum, we'll need to get the data from the
<code>
Wavelength
</code>
and
<code>
Flux
</code>
columns.
</p>
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<textarea aria-labelledby="cell-33-source-label nb-source-label" class="nb-source" form="cell-33" id="cell-33-source" name="source" readonly rows="7">#Take a peek at the FITS file we downloaded
filename = data['Local Path'][0]
fits.info(filename)
#Read the table with the spectrum from the FITS file
tab = Table.read(filename)
tab</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single">#Take a peek at the FITS file we downloaded</span>
<span class="n" title="Name">filename</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">data</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'Local Path'</span><span class="p" title="Punctuation">][</span><span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">fits</span><span class="o" title="Operator">.</span><span class="n" title="Name">info</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">filename</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single">#Read the table with the spectrum from the FITS file</span>
<span class="n" title="Name">tab</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">Table</span><span class="o" title="Operator">.</span><span class="n" title="Name">read</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">filename</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">tab</span>
</code></pre>
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<textarea aria-labelledby="cell-34-source-label nb-source-label" class="nb-source" form="cell-34" id="cell-34-source" name="source" readonly rows="1">### Plot the Spectrum</textarea>
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<pre><span></span><code><span class="gu" title="Generic.Subheading">### Plot the Spectrum</span>
</code></pre>
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Plot the Spectrum
</a>
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<textarea aria-labelledby="cell-35-source-label nb-source-label" class="nb-source" form="cell-35" id="cell-35-source" name="source" readonly rows="32"># Make the figure and set the font size globally
plt.rcParams.update({"font.size": 14})
plt.figure(1,(15,8))
# Gather the arrays from our data table
waves = tab['WAVELENGTH']
fluxes = tab["FLUX"]
segment = tab['SEGMENT']
# You'll notice from our data table that there are two segments to this observation, FUV A and FUV B
# Let's parse the spectra by their segment and plot them separately
ind_A = np.squeeze(np.where(fluxes != 0) and np.where(segment == 'FUVA'))
waves_A = waves[ind_A]
fluxes_A = fluxes[ind_A]
ind_B = np.squeeze(np.where(fluxes != 0) and np.where(segment == 'FUVB'))
waves_B = waves[ind_B]
fluxes_B = fluxes[ind_B]
# Plot both segments
plt.plot(waves_B, fluxes_B, label = "FUV B", color = 'lightcoral')
plt.plot(waves_A, fluxes_A, label = "FUV A", color = 'turquoise')
# Set the x and y axes labels and the title
plt.xlabel('Wavelength [{}]'.format(tab['WAVELENGTH'].unit))
plt.ylabel('Flux [{}]'.format(tab['FLUX'].unit))
plt.title("HST Spectrum of 3c273")
# Plot the legend
plt.legend()
# Give the figure a tight layout (optional)
plt.tight_layout()</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># Make the figure and set the font size globally</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">rcParams</span><span class="o" title="Operator">.</span><span class="n" title="Name">update</span><span class="p" title="Punctuation">({</span><span class="s2" title="Literal.String.Double">"font.size"</span><span class="p" title="Punctuation">:</span> <span class="mi" title="Literal.Number.Integer">14</span><span class="p" title="Punctuation">})</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">figure</span><span class="p" title="Punctuation">(</span><span class="mi" title="Literal.Number.Integer">1</span><span class="p" title="Punctuation">,(</span><span class="mi" title="Literal.Number.Integer">15</span><span class="p" title="Punctuation">,</span><span class="mi" title="Literal.Number.Integer">8</span><span class="p" title="Punctuation">))</span>
<span class="c1" title="Comment.Single"># Gather the arrays from our data table</span>
<span class="n" title="Name">waves</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">tab</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'WAVELENGTH'</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">fluxes</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">tab</span><span class="p" title="Punctuation">[</span><span class="s2" title="Literal.String.Double">"FLUX"</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">segment</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">tab</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'SEGMENT'</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># You'll notice from our data table that there are two segments to this observation, FUV A and FUV B</span>
<span class="c1" title="Comment.Single"># Let's parse the spectra by their segment and plot them separately</span>
<span class="n" title="Name">ind_A</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">squeeze</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">where</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">fluxes</span> <span class="o" title="Operator">!=</span> <span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">)</span> <span class="ow" title="Operator.Word">and</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">where</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">segment</span> <span class="o" title="Operator">==</span> <span class="s1" title="Literal.String.Single">'FUVA'</span><span class="p" title="Punctuation">))</span>
<span class="n" title="Name">waves_A</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">waves</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">ind_A</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">fluxes_A</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">fluxes</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">ind_A</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">ind_B</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">squeeze</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">where</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">fluxes</span> <span class="o" title="Operator">!=</span> <span class="mi" title="Literal.Number.Integer">0</span><span class="p" title="Punctuation">)</span> <span class="ow" title="Operator.Word">and</span> <span class="n" title="Name">np</span><span class="o" title="Operator">.</span><span class="n" title="Name">where</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">segment</span> <span class="o" title="Operator">==</span> <span class="s1" title="Literal.String.Single">'FUVB'</span><span class="p" title="Punctuation">))</span>
<span class="n" title="Name">waves_B</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">waves</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">ind_B</span><span class="p" title="Punctuation">]</span>
<span class="n" title="Name">fluxes_B</span> <span class="o" title="Operator">=</span> <span class="n" title="Name">fluxes</span><span class="p" title="Punctuation">[</span><span class="n" title="Name">ind_B</span><span class="p" title="Punctuation">]</span>
<span class="c1" title="Comment.Single"># Plot both segments</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">plot</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">waves_B</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">fluxes_B</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">label</span> <span class="o" title="Operator">=</span> <span class="s2" title="Literal.String.Double">"FUV B"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">color</span> <span class="o" title="Operator">=</span> <span class="s1" title="Literal.String.Single">'lightcoral'</span><span class="p" title="Punctuation">)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">plot</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">waves_A</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">fluxes_A</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">label</span> <span class="o" title="Operator">=</span> <span class="s2" title="Literal.String.Double">"FUV A"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">color</span> <span class="o" title="Operator">=</span> <span class="s1" title="Literal.String.Single">'turquoise'</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Set the x and y axes labels and the title</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">xlabel</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'Wavelength [</span><span class="si" title="Literal.String.Interpol">{}</span><span class="s1" title="Literal.String.Single">]'</span><span class="o" title="Operator">.</span><span class="n" title="Name">format</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">tab</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'WAVELENGTH'</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">unit</span><span class="p" title="Punctuation">))</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">ylabel</span><span class="p" title="Punctuation">(</span><span class="s1" title="Literal.String.Single">'Flux [</span><span class="si" title="Literal.String.Interpol">{}</span><span class="s1" title="Literal.String.Single">]'</span><span class="o" title="Operator">.</span><span class="n" title="Name">format</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">tab</span><span class="p" title="Punctuation">[</span><span class="s1" title="Literal.String.Single">'FLUX'</span><span class="p" title="Punctuation">]</span><span class="o" title="Operator">.</span><span class="n" title="Name">unit</span><span class="p" title="Punctuation">))</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">title</span><span class="p" title="Punctuation">(</span><span class="s2" title="Literal.String.Double">"HST Spectrum of 3c273"</span><span class="p" title="Punctuation">)</span>
<span class="c1" title="Comment.Single"># Plot the legend</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">legend</span><span class="p" title="Punctuation">()</span>
<span class="c1" title="Comment.Single"># Give the figure a tight layout (optional)</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">tight_layout</span><span class="p" title="Punctuation">()</span>
</code></pre>
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<textarea aria-labelledby="cell-36-source-label nb-source-label" class="nb-source" form="cell-36" id="cell-36-source" name="source" readonly rows="10"># Exercises
## Recognizing Familiar Emission Lines
Look at the spectrum we plotted, does anything stand out to you?
In astronomy, spectral features at specific wavelengths are indicative of known elements. For example, an emission line at 1216 Angstroms is called Lyman Alpha. It is produced when an orbital electron of a hydrogen atom drops from the first excited state down to the ground state, emitting a photon.
Does **3c273** have a Lyman Alpha emission line? Plot a vertical line at 1216 Angstroms to find out.
## Solution
This question is a bit disingenuous. We can add plot a vertical line at the Lyman Alpha wavelength, and we'll actually find quite a nice fit for our data.</textarea>
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<pre><span></span><code><span class="gh" title="Generic.Heading"># Exercises</span>
<span class="gu" title="Generic.Subheading">## Recognizing Familiar Emission Lines</span>
Look at the spectrum we plotted, does anything stand out to you?
In astronomy, spectral features at specific wavelengths are indicative of known elements. For example, an emission line at 1216 Angstroms is called Lyman Alpha. It is produced when an orbital electron of a hydrogen atom drops from the first excited state down to the ground state, emitting a photon.
Does <span class="gs" title="Generic.Strong">**3c273**</span> have a Lyman Alpha emission line? Plot a vertical line at 1216 Angstroms to find out.
<span class="gu" title="Generic.Subheading">## Solution</span>
This question is a bit disingenuous. We can add plot a vertical line at the Lyman Alpha wavelength, and we'll actually find quite a nice fit for our data.
</code></pre>
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<h1>
<a href="#exercises">
Exercises
</a>
</h1>
<h2>
<a href="#recognizing-familiar-emission-lines">
Recognizing Familiar Emission Lines
</a>
</h2>
<p>
Look at the spectrum we plotted, does anything stand out to you?
</p>
<p>
In astronomy, spectral features at specific wavelengths are indicative of known elements. For example, an emission line at 1216 Angstroms is called Lyman Alpha. It is produced when an orbital electron of a hydrogen atom drops from the first excited state down to the ground state, emitting a photon.
</p>
<p>
Does
<strong>
3c273
</strong>
have a Lyman Alpha emission line? Plot a vertical line at 1216 Angstroms to find out.
</p>
<h2>
<a href="#solution">
Solution
</a>
</h2>
<p>
This question is a bit disingenuous. We can add plot a vertical line at the Lyman Alpha wavelength, and we'll actually find quite a nice fit for our data.
</p>
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<textarea aria-labelledby="cell-37-source-label nb-source-label" class="nb-source" form="cell-37" id="cell-37-source" name="source" readonly rows="3"># appending these lines to the last cell above will give us the answer
xval = 1216 #Lyman Alpha in Angstroms
plt.axvline(xval, color = "black", linestyle = "--")</textarea>
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<pre><span></span><code><span class="c1" title="Comment.Single"># appending these lines to the last cell above will give us the answer</span>
<span class="n" title="Name">xval</span> <span class="o" title="Operator">=</span> <span class="mi" title="Literal.Number.Integer">1216</span> <span class="c1" title="Comment.Single">#Lyman Alpha in Angstroms</span>
<span class="n" title="Name">plt</span><span class="o" title="Operator">.</span><span class="n" title="Name">axvline</span><span class="p" title="Punctuation">(</span><span class="n" title="Name">xval</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">color</span> <span class="o" title="Operator">=</span> <span class="s2" title="Literal.String.Double">"black"</span><span class="p" title="Punctuation">,</span> <span class="n" title="Name">linestyle</span> <span class="o" title="Operator">=</span> <span class="s2" title="Literal.String.Double">"--"</span><span class="p" title="Punctuation">)</span>
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<textarea aria-labelledby="cell-38-source-label nb-source-label" class="nb-source" form="cell-38" id="cell-38-source" name="source" readonly rows="3">**However**, our target is an extragalactic quasar, so we should expect some redshifting. Indeed, the peak we've found here isn't from the quasar at all! It's contamination from a source along our line-of-sight.
The "true" Lyman Alpha emission is actually the enormous, broadened peak in FUV A. As an advanced exercise for the reader, you might try fitting a normal distribution to that peak, determining the center of the wavelength, then calculating the redshift using $$z = \frac{\lambda_{obs}-\lambda_{actual}}{\lambda_{actual}}$$</textarea>
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<pre><span></span><code><span class="gs" title="Generic.Strong">**However**</span>, our target is an extragalactic quasar, so we should expect some redshifting. Indeed, the peak we've found here isn't from the quasar at all! It's contamination from a source along our line-of-sight.
The "true" Lyman Alpha emission is actually the enormous, broadened peak in FUV A. As an advanced exercise for the reader, you might try fitting a normal distribution to that peak, determining the center of the wavelength, then calculating the redshift using $$z = \frac{\lambda_{obs}-\lambda_{actual}}{\lambda_{actual}}$$
</code></pre>
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3
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<p>
<strong>
However
</strong>
, our target is an extragalactic quasar, so we should expect some redshifting. Indeed, the peak we've found here isn't from the quasar at all! It's contamination from a source along our line-of-sight.
</p>
<p>
The "true" Lyman Alpha emission is actually the enormous, broadened peak in FUV A. As an advanced exercise for the reader, you might try fitting a normal distribution to that peak, determining the center of the wavelength, then calculating the redshift using $$z = \frac{\lambda_{obs}-\lambda_{actual}}{\lambda_{actual}}$$
</p>
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<textarea aria-labelledby="cell-39-source-label nb-source-label" class="nb-source" form="cell-39" id="cell-39-source" name="source" readonly rows="3"># Citations
* [Citation for `astropy`](https://github.com/astropy/astroquery/blob/main/astroquery/CITATION)
</textarea>
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<pre><span></span><code><span class="gh" title="Generic.Heading"># Citations</span>
<span class="k" title="Keyword">*</span><span class="w" title="Text.Whitespace"> </span>[<span class="nt" title="Name.Tag">Citation for `astropy`</span>](<span class="na" title="Name.Attribute">https://github.com/astropy/astroquery/blob/main/astroquery/CITATION</span>)
</code></pre>
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Citations
</a>
</h1>
<ul>
<li>
<a href="https://github.com/astropy/astroquery/blob/main/astroquery/CITATION">
Citation for
<code>
astropy
</code>
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<textarea aria-labelledby="cell-40-source-label nb-source-label" class="nb-source" form="cell-40" id="cell-40-source" name="source" readonly rows="11"># About this Notebook
**Author**: Emma Lieb &lt;br&gt;
**Last updated:** Apr 2023
If you have questions, comments, or other feedback, please contact the Archive HelpDesk at archive@stsci.edu.
***
&lt;img style="float: right;" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" alt="Space Telescope Logo" width="200px"/&gt;
[Return to top of page](#Historical-Quasar-Observations)</textarea>
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<pre><span></span><code><span class="gh" title="Generic.Heading"># About this Notebook</span>
<span class="gs" title="Generic.Strong">**Author**</span>: Emma Lieb &lt;br&gt;
<span class="gs" title="Generic.Strong">**Last updated:**</span> Apr 2023
If you have questions, comments, or other feedback, please contact the Archive HelpDesk at archive@stsci.edu.
***
&lt;img style="float: right;" src="https://raw.githubusercontent.com/spacetelescope/notebooks/master/assets/stsci_pri_combo_mark_horizonal_white_bkgd.png" alt="Space Telescope Logo" width="200px"/&gt;
[<span class="nt" title="Name.Tag">Return to top of page</span>](<span class="na" title="Name.Attribute">#Historical-Quasar-Observations</span>)
</code></pre>
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<h1>
<a href="#about-this-notebook">
About this Notebook
</a>
</h1>
<p>
<strong>
Author
</strong>
: Emma Lieb
<br>
<strong>
Last updated:
</strong>
Apr 2023
</p>
<p>
If you have questions, comments, or other feedback, please contact the Archive HelpDesk at
<a href="mailto:archive@stsci.edu">
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0
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252
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<textarea aria-labelledby="cell-22-source-label nb-source-label" class="nb-source" form="cell-22" id="cell-22-source" name="source" readonly rows="12">## closing thoughts
these notebooks have unexecuted code and `nbconvert-a11y` had to updated to handle that case. cf https://github.com/deathbeds/nbconvert-a11y/issues/33
currently i cannot handle site navigation~~ and licensing~~, but that is on the roadmap. (we want to think about them accessibly).
i'd like to use the jupyter toc as the toc to generate these templates.
this format for the documentation is a lot more flexible to modify than standard documentation systems.
we are dealing with our documentation as an intermediate table to offers introspection and manipulation.
search is a last thing to implement
are the colab and binder links even valid?
we'll need templates to handle myst admonitions</textarea>
<div aria-labelledby="nb-source-label" aria-roledescription="highlighted" role="group">
<div class="highlight">
<pre><span></span><code><span class="gu">## closing thoughts </span>
these notebooks have unexecuted code and <span class="sb">`nbconvert-a11y`</span> had to updated to handle that case. cf https://github.com/deathbeds/nbconvert-a11y/issues/33
currently i cannot handle site navigation~~ and licensing~~, but that is on the roadmap. (we want to think about them accessibly).
i'd like to use the jupyter toc as the toc to generate these templates.
this format for the documentation is a lot more flexible to modify than standard documentation systems.
we are dealing with our documentation as an intermediate table to offers introspection and manipulation.
search is a last thing to implement
are the colab and binder links even valid?
we'll need templates to handle myst admonitions
</code></pre>
</div>
</div>
</td>
<td class="nb-metadata" hidden role="none">
<button aria-controls="cell-22-metadata" aria-describedby="nb-metadata-desc" class="nb-metadata" form="cell-22" name="metadata" onclick="openDialog()">
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</button>
<dialog id="cell-22-metadata">
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<button formmethod="dialog">
Close
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<pre><code>
{'jp-MarkdownHeadingCollapsed': True, 'data': {'text/html': '<h2 id="closing-thoughts"><a href="#closing-thoughts">closing thoughts</a></h2>\n<p>these notebooks have unexecuted code and <code>nbconvert-a11y</code> had to updated to handle that case. cf <a href="https://github.com/deathbeds/nbconvert-a11y/issues/33">https://github.com/deathbeds/nbconvert-a11y/issues/33</a></p>\n<p>currently i cannot handle site navigation~~ and licensing~~, but that is on the roadmap. (we want to think about them accessibly).\ni\'d like to use the jupyter toc as the toc to generate these templates.\nthis format for the documentation is a lot more flexible to modify than standard documentation systems.\nwe are dealing with our documentation as an intermediate table to offers introspection and manipulation.</p>\n<p>search is a last thing to implement\nare the colab and binder links even valid?\nwe\'ll need templates to handle myst admonitions</p>\n'}}
</code></pre>
</form>
</dialog>
</td>
<td class="nb-loc" hidden id="cell-22-loc" role="none">
12
</td>
<td class="nb-outputs" role="none">
<h2 id="closing-thoughts">
<a href="#closing-thoughts">
closing thoughts
</a>
</h2>
<p>
these notebooks have unexecuted code and
<code>
nbconvert-a11y
</code>
had to updated to handle that case. cf
<a href="https://github.com/deathbeds/nbconvert-a11y/issues/33">
https://github.com/deathbeds/nbconvert-a11y/issues/33
</a>
</p>
<p>
currently i cannot handle site navigation~~ and licensing~~, but that is on the roadmap. (we want to think about them accessibly).
i'd like to use the jupyter toc as the toc to generate these templates.
this format for the documentation is a lot more flexible to modify than standard documentation systems.
we are dealing with our documentation as an intermediate table to offers introspection and manipulation.
</p>
<p>
search is a last thing to implement
are the colab and binder links even valid?
we'll need templates to handle myst admonitions
</p>
</td>
</tr>
<tr aria-labelledby="nb-cell-label 23 cell-23-cell_type" class="cell code" data-index="23" data-loc="0" role="listitem">
<th class="nb-anchor" role="none" scope="row">
<a aria-describedby="nb-code-label nb-cell-label cell-23-loc nb-loc-label" aria-labelledby="nb-cell-label 23" class="nb-anchor visually-hidden" href="#23" id="23">
23
</a>
</th>
<td class="nb-execution_count" hidden role="none">
<label class="nb-execution_count" id="cell-23-source-label">
<span>
In
</span>
<span aria-hidden="true">
[
</span>
<span>
None
</span>
<span aria-hidden="true">
]
</span>
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